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1.
Artigo em Inglês | MEDLINE | ID: mdl-32770737

RESUMO

BACKGROUND: There is no internationally vetted set of anatomic terms to describe human surface anatomy. OBJECTIVE: To establish expert consensus on a standardized set of terms that describe clinically-relevant human surface anatomy. METHODS: We conducted a Delphi consensus on surface anatomy terminology between July 2017 and July 2019. The initial survey included 385 anatomic terms, organized in 7 levels of hierarchy. If agreement exceeded the 75% established threshold, the term was considered 'accepted' and included in the final list. Terms added by the participants were passed on to the next round of consensus. Terms with less than 75% agreement were included in subsequent surveys along with alternative terms proposed by participants until agreement was reached on all terms. RESULTS: The Delphi included 21 participants. We found consensus (≥75% agreement) on 361/385 (93.8%) terms and eliminated one term in the first round. Of 49 new terms suggested by participants, 45 were added via consensus. To adjust for a recently published ICD-ST list of terms, a third survey including 111 discrepant terms was sent to participants. Finally, a total of 513 terms reached agreement via the Delphi method. CONCLUSIONS: We have established a set of 513 clinically-relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation.

2.
Nat Med ; 2020 Jun 22.
Artigo em Inglês | MEDLINE | ID: mdl-32572267

RESUMO

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice.

3.
J Am Acad Dermatol ; 2020 Jun 24.
Artigo em Inglês | MEDLINE | ID: mdl-32592885

RESUMO

BACKGROUND: A recently introduced dermatoscopic method for diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis (PAK) and solar lentigo/flat seborrheic keratosis (SL/SK). We term this the "inverse approach" OBJECTIVE: To determine whether training on the inverse approach increases the diagnostic accuracy of readers as compared to classic pattern analysis. METHODS: We used clinical and dermatoscopic images of histopathologically diagnosed LMs, PAKs and SLs/SKs. Participants of a dermatoscopy masterclass classified the lesions at baseline, after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 time points and to that of a trained convolutional neural network (CNN). RESULTS: The mean sensitivity for LM without training was 51.5%, after training on pattern analysis increased to 56.7% and after learning the inverse approach to 83.6%. The mean proportion of correct answers at the 3 time points was 62.1%, 65.5% and 78.5%. The percentage of readers outperforming the CNN was 6.4%, 15.4% and 53.9%, respectively. LIMITATIONS: The experimental setting and the inclusion of histopathologically diagnosed lesions only. CONCLUSIONS: The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.

4.
J Am Acad Dermatol ; 2020 Apr 29.
Artigo em Inglês | MEDLINE | ID: mdl-32360723

RESUMO

BACKGROUND: The number needed to biopsy (NNB) ratio for melanoma diagnosis is calculated by dividing the total number of biopsies by the number of biopsied melanomas. It is the inverse of positive predictive value (PPV), which is calculated by dividing the number of biopsied melanomas by the total number of biopsies. NNB is increasingly used as a metric to compare the diagnostic accuracy of health care practitioners. OBJECTIVE: To investigate the association of NNB with the standard statistical measures of sensitivity and specificity. METHODS: We extracted published diagnostic accuracy data from 5 cross-sectional skin cancer reader studies (median [min-max] readers/study was 29 [8-511]). Because NNB is a ratio, we converted it to PPV. RESULTS: Four studies showed no association and 1 showed a negative association between PPV and sensitivity. All 5 studies showed a positive association between PPV and specificity. LIMITATIONS: Reader study data. CONCLUSIONS: An individual health care practitioner with a lower NNB is likely to have a higher specificity than one with a higher NNB, assuming they practice under similar conditions; no conclusions can be made about their relative sensitivities. We advocate for additional research to define quality metrics for melanoma detection and caution when interpreting NNB.

6.
J Med Internet Res ; 22(1): e15597, 2020 01 24.
Artigo em Inglês | MEDLINE | ID: mdl-32012058

RESUMO

BACKGROUND: The diagnosis of pigmented skin lesion is error prone and requires domain-specific expertise, which is not readily available in many parts of the world. Collective intelligence could potentially decrease the error rates of nonexperts. OBJECTIVE: The aim of this study was to evaluate the feasibility and impact of collective intelligence for the detection of skin cancer. METHODS: We created a gamified study platform on a stack of established Web technologies and presented 4216 dermatoscopic images of the most common benign and malignant pigmented skin lesions to 1245 human raters with different levels of experience. Raters were recruited via scientific meetings, mailing lists, and social media posts. Education was self-declared, and domain-specific experience was tested by screening tests. In the target test, the readers had to assign 30 dermatoscopic images to 1 of the 7 disease categories. The readers could repeat the test with different lesions at their own discretion. Collective human intelligence was achieved by sampling answers from multiple readers. The disease category with most votes was regarded as the collective vote per image. RESULTS: We collected 111,019 single ratings, with a mean of 25.2 (SD 18.5) ratings per image. As single raters, nonexperts achieved a lower mean accuracy (58.6%) than experts (68.4%; mean difference=-9.4%; 95% CI -10.74% to -8.1%; P<.001). Collectives of nonexperts achieved higher accuracies than single raters, and the improvement increased with the size of the collective. A collective of 4 nonexperts surpassed single nonexperts in accuracy by 6.3% (95% CI 6.1% to 6.6%; P<.001). The accuracy of a collective of 8 nonexperts was 9.7% higher (95% CI 9.5% to 10.29%; P<.001) than that of single nonexperts, an improvement similar to single experts (P=.73). The sensitivity for malignant images increased for nonexperts (66.3% to 77.6%) and experts (64.6% to 79.4%) for answers given faster than the intrarater mean. CONCLUSIONS: A high number of raters can be attracted by elements of gamification and Web-based marketing via mailing lists and social media. Nonexperts increase their accuracy to expert level when acting as a collective, and faster answers correspond to higher accuracy. This information could be useful in a teledermatology setting.


Assuntos
Inteligência/genética , Neoplasias Cutâneas/diagnóstico , Telemedicina/métodos , Feminino , Humanos , Internet , Masculino , Neoplasias Cutâneas/patologia
8.
Skin Res Technol ; 2019 Dec 17.
Artigo em Inglês | MEDLINE | ID: mdl-31845429

RESUMO

BACKGROUND: Dermoscopic content-based image retrieval (CBIR) systems provide a set of visually similar dermoscopic (magnified and illuminated) skin images with a pathology-confirmed diagnosis for a given dermoscopic query image of a skin lesion. Although recent advances in machine learning have spurred novel CBIR algorithms, we have few insights into how end users interact with CBIRs and to what extent CBIRs can be useful for education and image interpretation. MATERIALS AND METHODS: We developed an interactive user interface for a CBIR system with dermoscopic images as a decision support tool and investigated users' interactions and decisions with the system. We performed a pilot experiment with 14 non-medically trained users for a given set of annotated dermoscopic images. RESULTS: Our pilot showed that the number of correct classifications and users' confidence levels significantly increased with the CBIR interface compared with a non-CBIR interface, although the timing also increased significantly. The users found the CBIR interface of high educational value, engaging and easy to use. CONCLUSION: Overall, users became more accurate, found the CBIR approach provided a useful decision aid, and had educational value for learning about skin conditions.

9.
Wien Klin Wochenschr ; 131(21-22): 582-583, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31713738
11.
Lancet Oncol ; 20(7): 938-947, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31201137

RESUMO

BACKGROUND: Whether machine-learning algorithms can diagnose all pigmented skin lesions as accurately as human experts is unclear. The aim of this study was to compare the diagnostic accuracy of state-of-the-art machine-learning algorithms with human readers for all clinically relevant types of benign and malignant pigmented skin lesions. METHODS: For this open, web-based, international, diagnostic study, human readers were asked to diagnose dermatoscopic images selected randomly in 30-image batches from a test set of 1511 images. The diagnoses from human readers were compared with those of 139 algorithms created by 77 machine-learning labs, who participated in the International Skin Imaging Collaboration 2018 challenge and received a training set of 10 015 images in advance. The ground truth of each lesion fell into one of seven predefined disease categories: intraepithelial carcinoma including actinic keratoses and Bowen's disease; basal cell carcinoma; benign keratinocytic lesions including solar lentigo, seborrheic keratosis and lichen planus-like keratosis; dermatofibroma; melanoma; melanocytic nevus; and vascular lesions. The two main outcomes were the differences in the number of correct specific diagnoses per batch between all human readers and the top three algorithms, and between human experts and the top three algorithms. FINDINGS: Between Aug 4, 2018, and Sept 30, 2018, 511 human readers from 63 countries had at least one attempt in the reader study. 283 (55·4%) of 511 human readers were board-certified dermatologists, 118 (23·1%) were dermatology residents, and 83 (16·2%) were general practitioners. When comparing all human readers with all machine-learning algorithms, the algorithms achieved a mean of 2·01 (95% CI 1·97 to 2·04; p<0·0001) more correct diagnoses (17·91 [SD 3·42] vs 19·92 [4·27]). 27 human experts with more than 10 years of experience achieved a mean of 18·78 (SD 3·15) correct answers, compared with 25·43 (1·95) correct answers for the top three machine algorithms (mean difference 6·65, 95% CI 6·06-7·25; p<0·0001). The difference between human experts and the top three algorithms was significantly lower for images in the test set that were collected from sources not included in the training set (human underperformance of 11·4%, 95% CI 9·9-12·9 vs 3·6%, 0·8-6·3; p<0·0001). INTERPRETATION: State-of-the-art machine-learning classifiers outperformed human experts in the diagnosis of pigmented skin lesions and should have a more important role in clinical practice. However, a possible limitation of these algorithms is their decreased performance for out-of-distribution images, which should be addressed in future research. FUNDING: None.

12.
JAMA Dermatol ; 2019 Jun 19.
Artigo em Inglês | MEDLINE | ID: mdl-31215969

RESUMO

Importance: The recent advances in the field of machine learning have raised expectations that computer-aided diagnosis will become the standard for the diagnosis of melanoma. Objective: To critically review the current literature and compare the diagnostic accuracy of computer-aided diagnosis with that of human experts. Data Sources: The MEDLINE, arXiv, and PubMed Central databases were searched to identify eligible studies published between January 1, 2002, and December 31, 2018. Study Selection: Studies that reported on the accuracy of automated systems for melanoma were selected. Search terms included melanoma, diagnosis, detection, computer aided, and artificial intelligence. Data Extraction and Synthesis: Evaluation of the risk of bias was performed using the QUADAS-2 tool, and quality assessment was based on predefined criteria. Data were analyzed from February 1 to March 10, 2019. Main Outcomes and Measures: Summary estimates of sensitivity and specificity and summary receiver operating characteristic curves were the primary outcomes. Results: The literature search yielded 1694 potentially eligible studies, of which 132 were included and 70 offered sufficient information for a quantitative analysis. Most studies came from the field of computer science. Prospective clinical studies were rare. Combining the results for automated systems gave a melanoma sensitivity of 0.74 (95% CI, 0.66-0.80) and a specificity of 0.84 (95% CI, 0.79-0.88). Sensitivity was lower in studies that used independent test sets than in those that did not (0.51; 95% CI, 0.34-0.69 vs 0.82; 95% CI, 0.77-0.86; P < .001); however, the specificity was similar (0.83; 95% CI, 0.71-0.91 vs 0.85; 95% CI, 0.80-0.88; P = .67). In comparison with dermatologists' diagnosis, computer-aided diagnosis showed similar sensitivities and a 10 percentage points lower specificity, but the difference was not statistically significant. Studies were heterogeneous and substantial risk of bias was found in all but 4 of the 70 studies included in the quantitative analysis. Conclusions and Relevance: Although the accuracy of computer-aided diagnosis for melanoma detection is comparable to that of experts, the real-world applicability of these systems is unknown and potentially limited owing to overfitting and the risk of bias of the studies at hand.

13.
JAMA Dermatol ; 155(1): 58-65, 2019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-30484822

RESUMO

Importance: Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose. Objective: To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience. Design, Setting, and Participants: A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy. Main Outcomes and Measures: The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures. Results: Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P < .001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P = .001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P = .18). Conclusions and Relevance: Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.


Assuntos
Algoritmos , Dermoscopia/métodos , Redes Neurais de Computação , Neoplasias Cutâneas/patologia , Adulto , Diagnóstico Diferencial , Feminino , Seguimentos , Humanos , Masculino , Pessoa de Meia-Idade , Curva ROC , Reprodutibilidade dos Testes , Estudos Retrospectivos , Pele/patologia
14.
Comput Biol Med ; 104: 111-116, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30471461

RESUMO

BACKGROUND AND OBJECTIVE: Fully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermatoscopic images. Our concept is that transferring encoder weights from a network trained on a classification task on images of the same domain may contain useful information for segmentation. METHODS: We trained a fully convolutional network where ResNet34 layers are reused as encoding layers of a U-Net style architecture. We entered the encoding layers i) with He uniform ("random") initialization, ii) pretrained ImageNet weights, or iii) after fine-tuning ResNet34 for skin lesion classification. After transferring the layers to the fully convolutional network architecture we trained for a binary segmentation task using official ISIC 2017 challenge data. RESULTS: Pretraining of ResNet34-layers with either ImageNet or fine-tuning for skin lesion classification achieved a higher Jaccard than random initialization (0.763 and 0.768 vs 0.740) on the ISIC 2017 test-set. This improved performance warrants further exploration on how to implement cross-task learning for skin lesion segmentation. In additional experiments we found that post-processing with fully connected conditional random fields consistently decreased Jaccard on ISIC 2017 test-set images despite reasonable visual results. Further exploration of the test-set revealed that conditional random field - post-processing decreased segmentation performance only if ground truth annotations consisted of simple shapes but increased it if shapes were complex. CONCLUSIONS: Our findings suggest that domain specific pretraining of encoders can be helpful when there are only few ground truth masks available for segmentation training, but may not be of additional benefit to ImageNet pretraining given enough segmentation training data. Complexity of ground truth annotations have a large impact on segmentation metrics and should be taken into account in skin lesion segmentation research.


Assuntos
Dermoscopia , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Dermatopatias , Pele/diagnóstico por imagem , Humanos , Dermatopatias/classificação , Dermatopatias/diagnóstico por imagem
15.
Australas J Dermatol ; 60(1): e33-e39, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-30109892

RESUMO

BACKGROUND AND OBJECTIVES: While dermatoscopy improves diagnostic accuracy for raised nonpigmented lesions, those with white surface keratin can be problematical. We investigated whether retention of povidone-iodine by surface keratin provides a clue to benignity. METHODS: We performed a retrospective pilot study (n = 57) followed by a prospective study (n = 117) on raised nonpigmented lesions with white surface keratin. An initial dermatoscopic image was taken of each lesion, povidone-iodine was applied and another image taken. Following lavage with 70% ethanol, a third image was acquired. The percentage surface area of residual povidone-iodine staining after lavage was recorded, and the results analysed. RESULTS: The optimal cut-off point of residual staining was 80%, where values of ≤80% pointed to malignancy. At this cut-off, the OR for lesions with values ≤80% to be truly malignant in the retrospective set was 4.03 (95% CI: 2.1-7.6) and the AUC was 0.7 (95% CI: 0.62-0.78). For the prospective set, the corresponding OR was 2.3 (95% CI: 1.4-3.7) and the AUC was 0.62 (95% CI: 0.55-0.68). CONCLUSIONS: This study presents evidence that povidone-iodine retention may have a degree of efficacy in distinguishing benign from malignant keratotic lesions. Further study is warranted.


Assuntos
Carcinoma de Células Escamosas/diagnóstico por imagem , Corantes , Dermoscopia/métodos , Ceratoacantoma/diagnóstico por imagem , Povidona-Iodo , Neoplasias Cutâneas/diagnóstico por imagem , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Carcinoma de Células Escamosas/patologia , Diagnóstico Diferencial , Feminino , Humanos , Ceratoacantoma/patologia , Masculino , Pessoa de Meia-Idade , Projetos Piloto , Estudos Prospectivos , Curva ROC , Estudos Retrospectivos , Neoplasias Cutâneas/patologia
16.
Exp Dermatol ; 27(11): 1261-1267, 2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30187575

RESUMO

While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered only a single clinical/macroscopic image and output a binary decision. In this work, we have presented a method which combines multiple imaging modalities together with patient metadata to improve the performance of automated skin lesion diagnosis. We evaluated our method on a binary classification task for comparison with previous studies as well as a five class classification task representative of a real-world clinical scenario. We showed that our multimodal classifier outperforms a baseline classifier that only uses a single macroscopic image in both binary melanoma detection (AUC 0.866 vs 0.784) and in multiclass classification (mAP 0.729 vs 0.598). In addition, we have quantitatively showed the automated diagnosis of skin lesions using dermatoscopic images obtains a higher performance when compared to using macroscopic images. We performed experiments on a new data set of 2917 cases where each case contains a dermatoscopic image, macroscopic image and patient metadata.


Assuntos
Aprendizado Profundo , Dermoscopia , Fotografação , Dermatopatias/classificação , Dermatopatias/diagnóstico , Humanos , Metadados , Imagem Multimodal
17.
Curr Treat Options Oncol ; 19(11): 56, 2018 09 20.
Artigo em Inglês | MEDLINE | ID: mdl-30238167

RESUMO

OPINION STATEMENT: Dermatoscopy (dermoscopy) improves the diagnosis of benign and malignant cutaneous neoplasms in comparison with examination with the unaided eye and should be used routinely for all pigmented and non-pigmented cutaneous neoplasms. It is especially useful for the early stage of melanoma when melanoma-specific criteria are invisible to the unaided eye. Preselection by the unaided eye is therefore not recommended. The increased availability of polarized dermatoscopes, and the extended use of dermatoscopy in non-pigmented lesions led to the discovery of new criteria, and we recommend that lesions should be examined with polarized and non-polarized dermatoscopy. The "chaos and clues algorithm" is a good starting point for beginners because it is easy to use, accurate, and it works for all types of pigmented lesions not only for those melanocytic. Physicians, who use dermatoscopy routinely, should be aware of new clues for acral melanomas, nail matrix melanomas, melanoma in situ, and nodular melanoma. Dermatoscopy should also be used to distinguish between different subtypes of basal cell carcinoma and to discriminate highly from poorly differentiated squamous cell carcinomas to optimize therapy and management of non-melanoma skin cancer. One of the most exciting areas of research is the use of dermatoscopic images for machine learning and automated diagnosis. Convolutional neural networks trained with dermatoscopic images are able to diagnose pigmented lesions with the same accuracy as human experts. We humans should not be afraid of this new and exciting development because it will most likely lead to a peaceful and fruitful coexistence of human experts and decision support systems.


Assuntos
Carcinoma Basocelular/diagnóstico , Carcinoma de Células Escamosas/diagnóstico , Dermoscopia/métodos , Ceratose Actínica/diagnóstico , Melanoma/diagnóstico , Neoplasias Cutâneas/diagnóstico , Pele/patologia , Adulto , Idoso , Algoritmos , Feminino , Humanos , Masculino , Sensibilidade e Especificidade
18.
Dermatol Pract Concept ; 8(3): 231-237, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30116670

RESUMO

Patients with multiple atypical nevi are at higher risk of developing melanoma. Among different techniques, sequential digital dermatoscopic imaging (SDDI) is a state-of-the art method to enhance diagnostic accuracy in evaluating pigmented skin lesions. It relies on analyzing digital dermatoscopic images of a lesion over time to find specific dynamic criteria inferring biologic behavior. SDDI can reduce the number of necessary excisions and finds melanomas in an early-and potentially curable-stage, but precautions in selecting patients and lesions have to be met to reach those goals.

19.
Sci Data ; 5: 180161, 2018 08 14.
Artigo em Inglês | MEDLINE | ID: mdl-30106392

RESUMO

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.


Assuntos
Transtornos da Pigmentação/diagnóstico por imagem , Dermatopatias/diagnóstico por imagem , Dermoscopia , Humanos , Processamento de Imagem Assistida por Computador , Transtornos da Pigmentação/patologia , Dermatopatias/patologia
20.
Stud Health Technol Inform ; 247: 850-854, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29678081

RESUMO

Medical terms of anatomical regions are not sufficient to unequivocally describe the locations of lesions on the skin. In order to get gender, age, height and weight independent localisations on the human skin we propose to use the base mesh of MakeHuman in combination with UV coordinates. As proof-of-concept we present a web application based on Three.js with three different MakeHuman 3D models (male, female, infant). Anatomical regions corresponding to UV coordinates are displayed and the UV coordinates of skin lesions can be marked. Marked regions are "transformed" to changed 3D models automatically allowing for tracking in spite of anatomic changes.


Assuntos
Imageamento Tridimensional , Dermatopatias , Pele , Feminino , Humanos , Masculino , Modelos Anatômicos
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