RESUMO
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.
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Biomarcadores Tumorais/análise , Aprendizado Profundo , Neoplasias/patologia , Tomada de Decisões , Humanos , Processamento de Imagem Assistida por Computador/métodos , Neoplasias/terapia , PrognósticoRESUMO
BACKGROUND: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. OBJECTIVE: The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. METHODS: We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%. RESULTS: A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin). CONCLUSIONS: Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.
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Inteligência Artificial/normas , Aprendizado Profundo/normas , Redes Neurais de Computação , Patologia/métodos , HumanosRESUMO
BACKGROUND: Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers. OBJECTIVE: This systematic review focuses on the current research investigating the impact of merging information from image features and patient data on the performance of CNN-based skin cancer image classification. This study aims to explore the potential in this field of research by evaluating the types of patient data used, the ways in which the nonimage data are encoded and merged with the image features, and the impact of the integration on the classifier performance. METHODS: Google Scholar, PubMed, MEDLINE, and ScienceDirect were screened for peer-reviewed studies published in English that dealt with the integration of patient data within a CNN-based skin cancer classification. The search terms skin cancer classification, convolutional neural network(s), deep learning, lesions, melanoma, metadata, clinical information, and patient data were combined. RESULTS: A total of 11 publications fulfilled the inclusion criteria. All of them reported an overall improvement in different skin lesion classification tasks with patient data integration. The most commonly used patient data were age, sex, and lesion location. The patient data were mostly one-hot encoded. There were differences in the complexity that the encoded patient data were processed with regarding deep learning methods before and after fusing them with the image features for a combined classifier. CONCLUSIONS: This study indicates the potential benefits of integrating patient data into CNN-based diagnostic algorithms. However, how exactly the individual patient data enhance classification performance, especially in the case of multiclass classification problems, is still unclear. Moreover, a substantial fraction of patient data used by dermatologists remains to be analyzed in the context of CNN-based skin cancer classification. Further exploratory analyses in this promising field may optimize patient data integration into CNN-based skin cancer diagnostics for patients' benefits.
Assuntos
Melanoma , Neoplasias Cutâneas , Dermoscopia , Humanos , Melanoma/diagnóstico , Redes Neurais de Computação , Neoplasias Cutâneas/diagnósticoRESUMO
BACKGROUND: Smoking is the largest preventable cause of mortality in Brazil. Education Against Tobacco (EAT) is a network of more than 3500 medical students and physicians across 14 countries who volunteer for school-based smoking prevention programs. EAT educates 50,000 adolescents per year in the classroom setting. A recent quasi-experimental study conducted in Germany showed that EAT had significant short-term smoking cessation effects among adolescents aged 11 to 15 years. OBJECTIVE: The aim is to measure the long-term effectiveness of the most recent version of the EAT curriculum in Brazil. METHODS: A randomized controlled trial was conducted among 2348 adolescents aged 12 to 21 years (grades 7-11) at public secondary schools in Brazil. The prospective experimental design included measurements at baseline and at 6 and 12 months postintervention. The study groups comprised randomized classes receiving the standardized EAT intervention (90 minutes of mentoring in a classroom setting) and control classes in the same schools (no intervention). Data were collected on smoking status, gender, social aspects, and predictors of smoking. The primary endpoint was the difference in the change in smoking prevalence between the intervention group and the control group at 12-month follow-up. RESULTS: From baseline to 12 months, the smoking prevalence increased from 11.0% to 20.9% in the control group and from 14.1% to 15.6% in the intervention group. This difference was statistically significant (P<.01). The effects were smaller for females (control 12.4% to 18.8% vs intervention 13.1% to 14.6%) than for males (control 9.1% to 23.6% vs intervention 15.3% to 16.8%). Increased quitting rates and prevented onset were responsible for the intervention effects. The differences in change in smoking prevalence from baseline to 12 months between the intervention and control groups were increased in students with low school performance. CONCLUSIONS: To our knowledge, this is the first randomized trial on school-based tobacco prevention in Brazil that shows significant long-term favorable effects. The EAT program encourages quitting and prevents smoking onset, especially among males and students with low educational background. TRIAL REGISTRATION: ClinicalTrials.gov NCT02725021; https://clinicaltrials.gov/ct2/show/NCT02725021. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.7134.
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Serviços de Saúde Escolar/normas , Abandono do Hábito de Fumar/métodos , Prevenção do Hábito de Fumar/métodos , Fumar Tabaco/prevenção & controle , Adolescente , Brasil , Criança , Feminino , Humanos , Masculino , Estudos Prospectivos , Instituições Acadêmicas , Estudantes de MedicinaRESUMO
BACKGROUND: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermatologists and could enable lifesaving and fast diagnoses, even outside the hospital via installation of apps on mobile devices. To our knowledge, at present there is no review of the current work in this research area. OBJECTIVE: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. METHODS: We searched the Google Scholar, PubMed, Medline, ScienceDirect, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. RESULTS: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large dataset and then optimize its parameters to the classification of skin lesions are the most common ones used and they display the best performance with the currently available limited datasets. CONCLUSIONS: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use nonpublic datasets for training and/or testing, thereby making reproducibility difficult. Future publications should use publicly available benchmarks and fully disclose methods used for training to allow comparability.
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Redes Neurais de Computação , Neoplasias Cutâneas/classificação , Humanos , Reprodutibilidade dos TestesRESUMO
A decreasing number of dermatologists and an increasing number of patients in Western countries have led to a relative lack of clinicians providing expert dermatologic care. This, in turn, has prolonged wait times for patients to be examined, putting them at risk. Store-and-forward teledermatology improves patient access to dermatologists through asynchronous consultations, reducing wait times to obtain a consultation. However, live video conferencing as a synchronous service is also frequently used by practitioners because it allows immediate interaction between patient and physician. This raises the question of which of the two approaches is superior in terms of quality of care and convenience. There are pros and cons for each in terms of technical requirements and features. This viewpoint compares the two techniques based on a literature review and a clinical perspective to help dermatologists assess the value of teledermatology and determine which techniques would be valuable in their practice.
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Dermatologia/métodos , Consulta Remota/métodos , Dermatopatias/diagnóstico , Telemedicina/métodos , Comunicação por Videoconferência/normas , Humanos , Dermatopatias/patologiaRESUMO
BACKGROUND: There is strong evidence for the effectiveness of addressing tobacco use in health care settings. However, few smokers receive cessation advice when visiting a hospital. Implementing smoking cessation technology in outpatient waiting rooms could be an effective strategy for change, with the potential to expose almost all patients visiting a health care provider without preluding physician action needed. OBJECTIVE: The objective of this study was to develop an intervention for smoking cessation that would make use of the time patients spend in a waiting room by passively exposing them to a face-aging, public morphing, tablet-based app, to pilot the intervention in a waiting room of an HIV outpatient clinic, and to measure the perceptions of this intervention among smoking and nonsmoking HIV patients. METHODS: We developed a kiosk version of our 3-dimensional face-aging app Smokerface, which shows the user how their face would look with or without cigarette smoking 1 to 15 years in the future. We placed a tablet with the app running on a table in the middle of the waiting room of our HIV outpatient clinic, connected to a large monitor attached to the opposite wall. A researcher noted all the patients who were using the waiting room. If a patient did not initiate app use within 30 seconds of waiting time, the researcher encouraged him or her to do so. Those using the app were asked to complete a questionnaire. RESULTS: During a 19-day period, 464 patients visited the waiting room, of whom 187 (40.3%) tried the app and 179 (38.6%) completed the questionnaire. Of those who completed the questionnaire, 139 of 176 (79.0%) were men and 84 of 179 (46.9%) were smokers. Of the smokers, 55 of 81 (68%) said the intervention motivated them to quit (men: 45, 68%; women: 10, 67%); 41 (51%) said that it motivated them to discuss quitting with their doctor (men: 32, 49%; women: 9, 60%); and 72 (91%) perceived the intervention as fun (men: 57, 90%; women: 15, 94%). Of the nonsmokers, 92 (98%) said that it motivated them never to take up smoking (men: 72, 99%; women: 20, 95%). Among all patients, 102 (22.0%) watched another patient try the app without trying it themselves; thus, a total of 289 (62.3%) of the 464 patients were exposed to the intervention (average waiting time 21 minutes). CONCLUSIONS: A face-aging app implemented in a waiting room provides a novel opportunity to motivate patients visiting a health care provider to quit smoking, to address quitting at their subsequent appointment and thereby encourage physician-delivered smoking cessation, or not to take up smoking.
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Envelhecimento/fisiologia , Face/fisiologia , Infecções por HIV/epidemiologia , Abandono do Hábito de Fumar/métodos , Adulto , Idoso , Instituições de Assistência Ambulatorial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Aplicativos Móveis , Projetos Piloto , Adulto JovemRESUMO
Development and homeostasis of the epidermis are governed by a complex network of sequence-specific transcription factors and epigenetic modifiers cooperatively regulating the subtle balance of progenitor cell self-renewal and terminal differentiation. To investigate the role of histone H2A deubiquitinase 2A-DUB/Mysm1 in the skin, we systematically analyzed expression, developmental functions, and potential interactions of this epigenetic regulator using Mysm1-deficient mice and skin-derived epidermal cells. Morphologically, skin of newborn and young adult Mysm1-deficient mice was atrophic with reduced thickness and cellularity of epidermis, dermis, and subcutis, in context with altered barrier function. Skin atrophy correlated with reduced proliferation rates in Mysm1-/- epidermis and hair follicles, and increased apoptosis compared with wild-type controls, along with increases in DNA-damage marker γH2AX. In accordance with diminished α6-Integrinhigh+CD34⺠epidermal stem cells, reduced colony formation of Mysm1-/- epidermal progenitors was detectable in vitro. On the molecular level, we identified p53 as potential mediator of the defective Mysm1-deficient epidermal compartment, resulting in increased pro-apoptotic and anti-proliferative gene expression. In Mysm1-/-p53-/- double-deficient mice, significant recovery of skin atrophy was observed. Functional properties of Mysm1-/- developing epidermis were assessed by quantifying the transepidermal water loss. In summary, this investigation uncovers a role for 2A-DUB/Mysm1 in suppression of p53-mediated inhibitory programs during epidermal development.
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Endopeptidases/metabolismo , Epiderme/embriologia , Epiderme/metabolismo , Proteína Supressora de Tumor p53/metabolismo , Animais , Apoptose/genética , Atrofia , Endopeptidases/genética , Epiderme/patologia , Expressão Gênica , Genótipo , Imuno-Histoquímica , Camundongos , Camundongos Knockout , Células-Tronco/metabolismo , Transativadores , Proteína Supressora de Tumor p53/genética , Proteases Específicas de UbiquitinaRESUMO
Smoking is the leading preventable cause of premature death in Germany. The network "Education Against Tobacco" (EAT) is an initiative that was founded in Germany in 2012, in which more than 3500 medical students and physicians engage in volunteer work in about 80 medical faculties in 14 countries. In this article, the concept, activities, objectives and associated research studies oft he EAT initiative are introduced.On the school level, the initiative addresses 10- to 15-year-old secondary school students. In addition to a multimodal approach, school visits use modern media such as facemorphing apps, which are not only used by students (45,000 per year in 14 countries), but by a total of over 500,000 other people as well. The effectiveness of the school-based intervention is currently being investigated in randomised long-term studies with 20,000 adolescents in Germany. A first long-term study demonstrated evidence of a protective effect regarding the onset of smoking, especially among female students, students having a low level of education and students with a migratory background.The programme educates several hundred prospective physicians at 13 (of 28 participating) German medical faculties each year in science-based elective courses for the well-established smoking cessation counselling of patients and sensitises them to the tobacco epidemic. The approved members engage in dialogue with local members of the German house of representatives as "Ärzteverband Tabakprävention".EAT motivates the prospective generation of physicians, initially through prevention in school settings, to face the challenge of national tobacco control at the university and federal level.
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Nicotiana , Abandono do Hábito de Fumar , Prevenção do Hábito de Fumar , Adolescente , Criança , Feminino , Alemanha , Humanos , Masculino , Estudos Prospectivos , Ensaios Clínicos Controlados Aleatórios como Assunto , EstudantesRESUMO
BACKGROUND: Around 90% of melanomas are caused by exposure to ultraviolet (UV) radiation and are therefore eminently preventable. Tanning behavior is mostly initiated in early adolescence, often with the belief that it increases attractiveness; the problems related to malignant melanoma and other skin cancers are too far in the future to fathom. Given the substantial amount of time children and adolescents spend in schools, as well as with their mobile phones, addressing melanoma prevention via both of these ways is crucial. However, no school-based intervention using mobile apps has been evaluated to date. We recently released a photoaging mobile app, in which a selfie is altered to predict future appearance dependent on UV protection behavior and skin type. OBJECTIVE: In this pilot study, we aimed to use mobile phone technology to improve school-based melanoma prevention and measure its preliminary success in different subgroups of students with regard to their UV protection behavior, Fitzpatrick skin type and age. METHODS: We implemented a free photoaging mobile phone app (Sunface) in 2 German secondary schools via a method called mirroring. We "mirrored" the students' altered 3-dimensional (3D) selfies reacting to touch on mobile phones or tablets via a projector in front of their whole grade. Using an anonymous questionnaire capturing sociodemographic data as well as risk factors for melanoma we then measured their perceptions of the intervention on a 5-point Likert scale among 205 students of both sexes aged 13-19 years (median 15 years). RESULTS: We measured more than 60% agreement in both items that measured motivation to reduce UV exposure and only 12.5% disagreement: 126 (63.0%) agreed or strongly agreed that their 3D selfie motivated them to avoid using a tanning bed, and 124 (61.7%) to increase use of sun protection. However, only 25 (12.5%) disagreed with both items. The perceived effect on motivation was increased in participants with Fitzpatrick skin types 1-2 in both tanning bed avoidance (n=74, 71.8% agreement in skin types 1-2 vs n=50, 53.8% agreement in skin types 3-6) and increased use of sun protection (n=70, 68.0% agreement in skin types 1-2 vs n=52, 55.3% agreement in skin types 3-6), and also positively correlated with higher age. CONCLUSIONS: We present a novel way of integrating photoaging in school-based melanoma prevention that affects the students' peer group, considers the predictors of UV exposure in accordance with the theory of planned behavior, and is particularly effective in changing behavioral predictors in fair-skinned adolescents (Fitzpatrick skin types 1-2). Further research is required to evaluate the intervention's prospective effects on adolescents of various cultural backgrounds.
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Melanoma/prevenção & controle , Aplicativos Móveis/estatística & dados numéricos , Serviços de Saúde Escolar/normas , Neoplasias Cutâneas/prevenção & controle , Adolescente , Criança , Feminino , Educação em Saúde , Humanos , Masculino , Melanoma/patologia , Fotografação , Projetos Piloto , Estudos Prospectivos , Neoplasias Cutâneas/patologia , Inquéritos e QuestionáriosRESUMO
This viewpoint reviews the perspectives for dermatology as a specialty to go beyond the substantial impact of smoking on skin disease and leverage the impact of skin changes on a person's self-concept and behavior in the design of effective interventions for smoking prevention and cessation.
Assuntos
Dermatologistas/normas , Aplicativos Móveis/estatística & dados numéricos , Envelhecimento da Pele/fisiologia , Prevenção do Hábito de Fumar/métodos , Fumar/terapia , HumanosRESUMO
BACKGROUND: More than 8.5 million Germans suffer from chronic diseases attributable to smoking. Education Against Tobacco (EAT) is a multinational network of medical students who volunteer for school-based prevention in the classroom setting, amongst other activities. EAT has been implemented in 28 medical schools in Germany and is present in 13 additional countries around the globe. A recent quasi-experimental study showed significant short-term smoking cessation effects on 11-to-15-year-old adolescents. OBJECTIVE: The aim of this study was to provide the first randomized long-term evaluation of the optimized 2014 EAT curriculum involving a photoaging software for its effectiveness in reducing the smoking prevalence among 11-to-15-year-old pupils in German secondary schools. METHODS: A randomized controlled trial was undertaken with 1504 adolescents from 9 German secondary schools, aged 11-15 years in grades 6-8, of which 718 (47.74%) were identifiable for the prospective sample at the 12-month follow-up. The experimental study design included measurements at baseline (t1), 6 months (t2), and 12 months postintervention (t3), via questionnaire. The study groups consisted of 40 randomized classes that received the standardized EAT intervention (two medical student-led interactive modules taking 120 minutes total) and 34 control classes within the same schools (no intervention). The primary endpoint was the difference in smoking prevalence from t1 to t3 in the control group versus the difference from t1 to t3 in the intervention group. The differences in smoking behavior (smoking onset, quitting) between the two groups, as well as gender-specific effects, were studied as secondary outcomes. RESULTS: None of the effects were significant due to a high loss-to-follow-up effect (52.26%, 786/1504). From baseline to the two follow-up time points, the prevalence of smoking increased from 3.1% to 5.2% to 7.2% in the control group and from 3.0% to 5.4% to 5.8% in the intervention group (number needed to treat [NNT]=68). Notable differences were observed between the groups for the female gender (4.2% to 9.5% for control vs 4.0% to 5.2% for intervention; NNT=24 for females vs NNT=207 for males), low educational background (7.3% to 12% for control vs 6.1% to 8.7% for intervention; NNT=30), and migrational background (students who claimed that at least one parent was not born in Germany) at the 12-month follow-up. The intervention appears to prevent smoking onset (NNT=63) but does not appear to initiate quitting. CONCLUSIONS: The intervention appears to prevent smoking, especially in females and students with a low educational background.
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Serviços de Saúde Escolar/estatística & dados numéricos , Instituições Acadêmicas/estatística & dados numéricos , Abandono do Hábito de Fumar/métodos , Prevenção do Hábito de Fumar/métodos , Fumar/psicologia , Estudantes de Medicina/psicologia , Adolescente , Criança , Feminino , Alemanha , Humanos , Masculino , Estudos Prospectivos , Projetos de Pesquisa , Fumar/epidemiologia , Inquéritos e Questionários , NicotianaRESUMO
BACKGROUND: Most smokers start smoking during their early adolescence, often with the idea that smoking is glamorous. Adolescent smoking can best be prevented through health education at schools. Interventions that take advantage of the broad availability of mobile phones as well as adolescents' interest in their appearance may be a novel way to improve prevention. OBJECTIVE: In this first pilot study, we aimed to use mobile phone technology in accordance with the theory of planned behavior to improve school-based tobacco prevention. METHODS: We used a free photoaging mobile phone app ("Smokerface") in three German secondary schools via a novel method called mirroring. The students' altered three-dimensional selfies on mobile phones or tablets were "mirrored" via a projector in front of their whole grade. Using an anonymous questionnaire, we then measured on a 5-point Likert scale the perceptions of the intervention among 125 students of both genders (average age 12.75 years). RESULTS: A majority of the students perceived the intervention as fun (77/125, 61.6%), claimed that the intervention motivated them not to smoke (79/125, 63.2%), and stated that they learned new benefits of non-smoking (81/125, 64.8%). Only a minority of students disagreed or fully disagreed that they learned new benefits of non-smoking (16/125, 12.8%) or that they were themselves motivated not to smoke (18/125, 14.4%). CONCLUSIONS: We have presented a novel method to integrate photoaging in school-based tobacco prevention that affects student peer groups and considers the predictors of smoking in accordance with the theory of planned behavior.
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Envelhecimento , Educação em Saúde , Aplicativos Móveis , Fotografação , Serviços de Saúde Escolar , Prevenção do Hábito de Fumar , Adolescente , Telefone Celular , Criança , Feminino , Humanos , Masculino , Motivação , Grupo Associado , Projetos Piloto , Instituições Acadêmicas , Estudantes , Inquéritos e QuestionáriosRESUMO
BACKGROUND: Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network's uncertainty together with its prediction. OBJECTIVE: In this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation. METHODS: Google Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published between 2016 and 2021, that deal with uncertainty estimation in medical image classification. The search terms "uncertainty," "uncertainty estimation," "network calibration," and "out-of-distribution detection" were used in combination with the terms "medical images," "medical image analysis," and "medical image classification." RESULTS: A total of 22 papers were chosen for detailed analysis through the systematic review process. This paper provides a table for a systematic comparison of the included works with respect to the applied method for estimating the uncertainty. CONCLUSIONS: The applied methods for estimating uncertainties are diverse, but the sampling-based methods Monte-Carlo Dropout and Deep Ensembles are used most frequently. We concluded that future works can investigate the benefits of uncertainty estimation in collaborative settings of artificial intelligence systems and human experts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11936.
RESUMO
BACKGROUND: Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. OBJECTIVE: We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. METHODS: We recruited participants below 35 years of age using three social media channels-Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants' attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants' preferences. We evaluated potential concerns using maximum difference scaling. RESULTS: We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. CONCLUSIONS: The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants' demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.
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Aplicativos Móveis , Neoplasias Cutâneas , Inteligência Artificial , Teorema de Bayes , Exercício Físico , Humanos , Neoplasias Cutâneas/diagnóstico , Adulto JovemRESUMO
BACKGROUND: Data on how SARS-CoV-2 enters and spreads in a population are essential for guiding public policies. OBJECTIVE: This study seeks to understand the transmission dynamics of SARS-CoV-2 in small Brazilian towns during the early phase of the epidemic and to identify core groups that can serve as the initial source of infection as well as factors associated with a higher risk of COVID-19. METHODS: Two population-based seroprevalence studies, one household survey, and a case-control study were conducted in two small towns in southeastern Brazil between May and June 2020. In the population-based studies, 400 people were evaluated in each town; there were 40 homes in the household survey, and 95 cases and 393 controls in the case-control study. SARS-CoV-2 serology testing was performed on participants, and a questionnaire was applied. Prevalence, household secondary infection rate, and factors associated with infection were assessed. Odds ratios (ORs) were calculated by logistic regression. Logistics worker was defined as an individual with an occupation focused on the transportation of people or goods and whose job involves traveling outside the town of residence at least once a week. RESULTS: Higher seroprevalence of SARS-CoV-2 was observed in the town with a greater proportion of logistics workers. The secondary household infection rate was 49.1% (55/112), and it was observed that in most households (28/40, 70%) the index case was a logistics worker. The case-control study revealed that being a logistics worker (OR 18.0, 95% CI 8.4-38.7) or living with one (OR 6.9, 95% CI 3.3-14.5) increases the risk of infection. In addition, having close contact with a confirmed case (OR 13.4, 95% CI 6.6-27.3) and living with more than four people (OR 2.7, 95% CI 1.1-7.1) were also risk factors. CONCLUSIONS: Our study shows a strong association between logistics workers and the risk of SARS-CoV-2 infection and highlights the key role of these workers in the viral spread in small towns. These findings indicate the need to focus on this population to determine COVID-19 prevention and control strategies, including vaccination and sentinel genomic surveillance.
Assuntos
COVID-19/epidemiologia , COVID-19/transmissão , Doenças Transmissíveis Importadas/epidemiologia , Ocupações/estatística & dados numéricos , Meios de Transporte/estatística & dados numéricos , Adolescente , Adulto , Brasil/epidemiologia , Estudos de Casos e Controles , Criança , Pré-Escolar , Cidades/epidemiologia , Características da Família , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Pessoa de Meia-Idade , Fatores de Risco , Estudos Soroepidemiológicos , Adulto JovemRESUMO
BACKGROUND: In the emerging era of digitalization and electronic health, various health-related apps have been launched, including apps for sexually transmitted diseases. Until now, little has been known about how patients perceive the value of such apps. OBJECTIVE: To investigate patient's attitudes and awareness toward sexually transmitted disease-related apps in an outpatient sexually transmitted disease clinic setting. METHODS: A cross-sectional study was conducted at a dermatovenereological outpatient unit between April and July 2019. Patients completed a self-administered questionnaire on their perceptions of the popularity and usefulness of sexually transmitted disease-related apps. Descriptive analysis was performed with expression of categorical variables as frequencies and percentages. For continuous variables, the median, range, and interquartile range were indicated. Contingency tables and chi-square tests were used to investigate associations between sociodemographic data and items of the questionnaire. RESULTS: A total of 226 patients were surveyed (heterosexual: 137/193, 71.0%; homosexual: 44/193, 22.8%; bisexual: 12/193, 6.2%); 11.9% (27/225) had previously used health-related apps. Nearly half of the patients (97/214, 45.3%) specifically considered sexually transmitted disease-related apps useful, 47.8% (100/209) voted that they could supplement or support the consultation of a physician. Interestingly, only 35.1% (74/211) preferred a printed patient brochure on sexually transmitted diseases over downloading and using an app, but 64.0% (134/209) would download a sexually transmitted disease-related app recommended by their physician. General information regarding sexually transmitted diseases (93/167, 55.7%), evaluation of skin diseases based on photos or videos (78/167, 53.3%), information on the prevention of sexually transmitted diseases (76/167, 45.5%), mediation of nearby contact points or test sites (74/167, 44.3%), anonymous medical advice (69/167, 41.3%), and calculation of the risk of having a sexually transmitted disease (63/167, 37.3%) were rated as the most important features. Men were more likely than women to find sexually transmitted disease-related apps useful in general (P=.04; χ2=6.28) and to pay for such apps (P=.01; χ2=9.19). Patients aged <40 years would rather download an app recommended by their physician (P=.03; χ2=7.23), whereas patients aged >40 years preferred reading a patient brochure on sexually transmitted diseases (P=.02; χ2=8.14). CONCLUSIONS: This study demonstrated high general interest in the use of sexually transmitted disease-related apps in this sample of dermatovenereological outpatients. In particular, young age and male sex were significantly associated with a positive perception, underlining the high potential of apps in the prevention and early recognition of sexually transmitted diseases in this group. Future studies are warranted to validate these findings in other populations.
Assuntos
Telefone Celular , Aplicativos Móveis , Infecções Sexualmente Transmissíveis , Idoso , Estudos Transversais , Feminino , Humanos , Masculino , Percepção , Infecções Sexualmente Transmissíveis/prevenção & controleRESUMO
BACKGROUND: In the emerging era of digitalization and electronic health, skin cancer-related apps represent useful tools to support dermatologic consultation and examination. Yet, little is known about how patients perceive the value of such apps. OBJECTIVE: The aim of this study was to investigate patient attitudes and their awareness toward skin cancer-related apps. METHODS: A cross-sectional study including 200 patients from the oncological outpatient unit was conducted at the University Hospital (LMU Munich, Germany) between September and December 2018. Patients were asked to complete a self-administered questionnaire on the popularity and usefulness of health-related and skin cancer-related apps. A descriptive analysis was performed with the expression of categorical variables as frequencies and percentages. For continuous variables, the median and range were indicated. Contingency tables and chi-square tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire. RESULTS: A total of 98.9% (195/197) of patients had never used skin cancer-related apps or could not remember. In 49.7% (93/187) of cases, patients were unsure about the usefulness of skin cancer apps, whereas 42.6% (78/183) thought that skin cancer apps could supplement or support the professional skin examination performed by a physician. However, 47.9% (90/188) were interested in acquiring more information by their dermatologists about skin cancer apps. Young age (P=.002), male gender (P=.02), a previous history of melanoma (P=.004), and higher educational level (P=.002) were significantly associated with a positive attitude. Nevertheless, 55.9% (105/188) preferred a printed patient brochure on skin cancer to downloading and using an app. CONCLUSIONS: The experience and knowledge of skin cancer-related apps was surprisingly low in this population, although there was a high general interest in more information about such apps. Printed patient brochures were the preferred information source.
Assuntos
Aplicativos Móveis/normas , Pacientes/psicologia , Higiene da Pele/instrumentação , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos Transversais , Feminino , Letramento em Saúde/normas , Letramento em Saúde/estatística & dados numéricos , Humanos , Masculino , Pessoa de Meia-Idade , Aplicativos Móveis/estatística & dados numéricos , Pacientes/estatística & dados numéricos , Higiene da Pele/métodos , Higiene da Pele/normas , Neoplasias Cutâneas/prevenção & controle , Neoplasias Cutâneas/psicologia , Inquéritos e QuestionáriosRESUMO
Smoking is a major global risk factor for preventable death and disability. EAT is an acronym for Education Against Tobacco, a multinational network of physicians and medical students that aims to improve tobacco control by means of school-based prevention targeted at adolescents through counseling, use of software and support materials. The first EAT-Brazil Award, launched in March 2018, was a competition designed to encourage the proposal of objective solutions for tobacco control in Brasil, and identify new talents in the area. Brazilian undergraduate students from any field of study could submit a one-page essay on the subject, competing for the amount of R$ 1000.00 (one thousand reais). There were a total of 39 applicants (20 women and 19 men) from 9 Brazilian states and 18 undergraduate programs, with a mean age of 22.5 years (SD = 3.7). Data from an online anonymous questionnaire answered after the submission of their essays revealed that most applicants were students of institutions from in the state of Minas Gerais (n = 26/39; 66.6%), studied medicine (n = 20/39, 51.3%), and had no prior knowledge of the EAT-Brazil Network (n = 27/39, 69.2%). The winner of the award was Lucas Guimarães de Azevedo, a fourth-year medical student at Federal University of Western Bahia. The next editions of the award should focus on increasing the number of applicants and diversifying their geographical distribution.
Assuntos
Distinções e Prêmios , Prevenção do Hábito de Fumar/métodos , Estudantes/estatística & dados numéricos , Adolescente , Adulto , Brasil , Feminino , Humanos , Masculino , Inquéritos e Questionários , Adulto JovemRESUMO
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25-26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis. METHODS: Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels. FINDINGS: The total discordance with the histopathologist was 18% for melanoma (95% confidence interval [CI]: 7.4-28.6%), 20% for nevi (95% CI: 8.9-31.1%) and 19% for the full set of images (95% CI: 11.3-26.7%). INTERPRETATION: Even in the worst case, the discordance of the CNN was about the same compared with the discordance between human pathologists as reported in the literature. Despite the vastly reduced amount of data, time necessary for diagnosis and cost compared with the pathologist, our CNN archived on-par performance. Conclusively, CNNs indicate to be a valuable tool to assist human melanoma diagnoses.