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1.
Zhonghua Yan Ke Za Zhi ; 57(6): 447-453, 2021 Jun 11.
Artigo em Chinês | MEDLINE | ID: mdl-34098694

RESUMO

Objective: To developed an image analysis system of anterior segment optical coherence tomography (AS-OCT) examination results based on deep learning technology, and to evaluate its effect in identifying various types of corneal pathologies and quantified indices. Methods: A total of 4 026 patients (5 617 eyes), including 1 977 males and 2 049 females, aged (45±23) years, were enrolled in Qingdao Eye Hospital from January 2011 to August 2019. The AS-OCT images were used as a training dataset, which were labeled with location information of 16 corneal pathologies (including corneal epithelial defect, corneal epithelial thickening, corneal thinning and so on) by clinical experts, as well as the tissue stratification of the corneal epithelium and stroma. The labeled AS-OCT images were used to train the corneal pathology detection model and corneal stratification model based on deep convolutional neural network algorithm. Then 1 709 AS-OCT images of the affected eyes were collected as a validation dataset. Compared with the artificial labeling results, the accuracy, sensitivity and specificity were evaluated in the corneal pathology detection model, and the overlapping rate (Dice coefficient) between the labeled area of the model and the artificial labeling area was used to evaluate the corneal stratification model. Results: The results of 5 617 training sets showed that there were 1 472 cases of corneal epithelial defect, 2 416 cases of corneal epithelial thickening, 2 001 cases of corneal thinning, 780 cases of corneal lordosis, 2 064 cases of corneal thickening, 358 cases of subepithelial blisters, 486 cases of subepithelial opacity, 1 010 cases of corneal ulcer, 3 635 cases of stromal opacity, 1 060 cases of posterior elastic layer fold, 137 cases of posterior elastic layer detachment, 665 cases of keratic precipitate, 176 cases of corneal perforation, 127 cases of corneal foreign body, 299 cases of after lamellar keratoplasty (LKP) and 234 cases of after penetrating keratoplasty (PKP). Among 1 709 images, 1 596 were manually labeled. The average sensitivity and specificity of the corneal pathology detection model were 96.5% and 96.1% compared with the results of manual labeling. Fifteen samples were missed for detection, and the rate was 0.93%. The average Dice coefficients of the corneal stratification model for the corneal epithelium and stroma were 0.985 and 0.917, respectively. Conclusions: Our artificial intelligence-based diagnosis system with AS-OCT is able to give quantified information and location information of corneal lesions with high accuracy, which can help ophthalmologists improve the efficiency and accuracy of diagnosis. (Chin J Ophthalmol, 2021, 57: 447-453).


Assuntos
Aprendizado Profundo , Ceratocone , Inteligência Artificial , Feminino , Humanos , Ceratocone/diagnóstico por imagem , Masculino , Estudos Retrospectivos , Tomografia de Coerência Óptica
2.
Zhonghua Yan Ke Za Zhi ; 57(6): 459-464, 2021 Jun 11.
Artigo em Chinês | MEDLINE | ID: mdl-34098696

RESUMO

Choroidal thinning is an important feature of high myopia and has a negative correlation with the degree of myopia. However, due to the limitations of choroidal imaging, specific changes in choroidal thickness and vasculature are unclear. In recent years, the development of optical coherence tomography technology and optical coherence tomography angiography technology has made it possible to solve the problem. Emergence of biomarkers that objectively quantify choroidal thickness and vascular changes will help us understand the pathogenesis of high myopia and provide new ideas for the prognosis and treatment of myopia. In this review, in order to provide reference for clinical work, we summarize recent advances in the application of the two technologies in observing morphological changes of the choroid in high myopia and discuss the problems and prospects when they are combined with artificial intelligence for choroidal imaging. (Chin J Ophthalmol, 2021, 57: 459-464).


Assuntos
Inteligência Artificial , Miopia , Angiografia , Corioide/diagnóstico por imagem , Humanos , Tomografia de Coerência Óptica
3.
Zhonghua Yan Ke Za Zhi ; 57(6): 465-469, 2021 Jun 11.
Artigo em Chinês | MEDLINE | ID: mdl-34098697

RESUMO

The application of artificial intelligence (AI) in ophthalmology will greatly reduce the workload of ophthalmologists. Machine learning is an important branch of AI, and deep learning is the most important algorithm in machine learning. At present, AI is well applied in the ophthalmic field. This article summarizes the use of AI in ophthalmology and discusses its inadequacy and future to provide reference for clinical practice. (Chin J Ophthalmol, 2021, 57: 465-469).


Assuntos
Oftalmologistas , Oftalmologia , Inteligência Artificial , Olho , Humanos , Aprendizado de Máquina
4.
Cien Saude Colet ; 26(5): 1885-1898, 2021 May.
Artigo em Português, Inglês | MEDLINE | ID: mdl-34076129

RESUMO

This article explores the use of spatial artificial intelligence to estimate the resources needed to implement Brazil's COVID-19 immu nization campaign. Using secondary data, we conducted a cross-sectional ecological study adop ting a time-series design. The unit of analysis was Brazil's primary care centers (PCCs). A four-step analysis was performed to estimate the popula tion in PCC catchment areas using artificial in telligence algorithms and satellite imagery. We also assessed internet access in each PCC and con ducted a space-time cluster analysis of trends in cases of SARS linked to COVID-19 at municipal level. Around 18% of Brazil's elderly population live more than 4 kilometer from a vaccination point. A total of 4,790 municipalities showed an upward trend in SARS cases. The number of PCCs located more than 5 kilometer from cell towers was largest in the North and Northeast regions. Innovative stra tegies are needed to address the challenges posed by the implementation of the country's National COVID-19 Vaccination Plan. The use of spatial artificial intelligence-based methodologies can help improve the country's COVID-19 response.


Assuntos
Vacinas contra COVID-19 , COVID-19 , Idoso , Inteligência Artificial , Brasil , Cidades , Estudos Transversais , Humanos , Inteligência , SARS-CoV-2 , Vacinação
5.
Sensors (Basel) ; 21(11)2021 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-34063974

RESUMO

The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m3, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety.


Assuntos
Galinhas , Aprendizado Profundo , Animais , Inteligência Artificial , Humanos , Aves Domésticas , Taiwan
6.
BMC Med Inform Decis Mak ; 21(1): 178, 2021 06 03.
Artigo em Inglês | MEDLINE | ID: mdl-34082719

RESUMO

BACKGROUND: Artificial Intelligence has the potential to revolutionize healthcare, and it is increasingly being deployed to support and assist medical diagnosis. One potential application of AI is as the first point of contact for patients, replacing initial diagnoses prior to sending a patient to a specialist, allowing health care professionals to focus on more challenging and critical aspects of treatment. But for AI systems to succeed in this role, it will not be enough for them to merely provide accurate diagnoses and predictions. In addition, it will need to provide explanations (both to physicians and patients) about why the diagnoses are made. Without this, accurate and correct diagnoses and treatments might otherwise be ignored or rejected. METHOD: It is important to evaluate the effectiveness of these explanations and understand the relative effectiveness of different kinds of explanations. In this paper, we examine this problem across two simulation experiments. For the first experiment, we tested a re-diagnosis scenario to understand the effect of local and global explanations. In a second simulation experiment, we implemented different forms of explanation in a similar diagnosis scenario. RESULTS: Results show that explanation helps improve satisfaction measures during the critical re-diagnosis period but had little effect before re-diagnosis (when initial treatment was taking place) or after (when an alternate diagnosis resolved the case successfully). Furthermore, initial "global" explanations about the process had no impact on immediate satisfaction but improved later judgments of understanding about the AI. Results of the second experiment show that visual and example-based explanations integrated with rationales had a significantly better impact on patient satisfaction and trust than no explanations, or with text-based rationales alone. As in Experiment 1, these explanations had their effect primarily on immediate measures of satisfaction during the re-diagnosis crisis, with little advantage prior to re-diagnosis or once the diagnosis was successfully resolved. CONCLUSION: These two studies help us to draw several conclusions about how patient-facing explanatory diagnostic systems may succeed or fail. Based on these studies and the review of the literature, we will provide some design recommendations for the explanations offered for AI systems in the healthcare domain.


Assuntos
Médicos , Confiança , Inteligência Artificial , Pessoal de Saúde , Humanos , Satisfação Pessoal
7.
Sensors (Basel) ; 21(11)2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34071944

RESUMO

The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the "Internet of Medical Things" (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.


Assuntos
Inteligência Artificial , Internet das Coisas , Algoritmos , Humanos , Aprendizado de Máquina
8.
Sensors (Basel) ; 21(9)2021 May 05.
Artigo em Inglês | MEDLINE | ID: mdl-34062961

RESUMO

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Atmosféricos/análise , Poluição do Ar/análise , Inteligência Artificial , Calibragem , Cidades , Monitoramento Ambiental , Humanos
9.
Sensors (Basel) ; 21(10)2021 May 12.
Artigo em Inglês | MEDLINE | ID: mdl-34066186

RESUMO

Wearable technologies are becoming a profitable means of monitoring a person's health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker's health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators' security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators' health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.


Assuntos
Dispositivos Eletrônicos Vestíveis , Local de Trabalho , Inteligência Artificial , Atividades Humanas , Humanos , Monitorização Fisiológica
10.
Sensors (Basel) ; 21(9)2021 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-34067084

RESUMO

Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses' possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Algoritmos , Animais , Computadores , Software
11.
Zhonghua Bing Li Xue Za Zhi ; 50(6): 620-625, 2021 Jun 08.
Artigo em Chinês | MEDLINE | ID: mdl-34078050

RESUMO

Objective: To investigate the value of deep learning in classifying non-inflammatory aortic membrane degeneration. Methods: Eighty-nine cases of non-inflammatory aortic media degeneration diagnosed from January to June 2018 were collected at Beijing Anzhen Hospital, Capital Medical University, China and scanned into digital sections. 1 627 hematoxylin and eosin stained photomicrographs were extracted. Combined with the ResNet18-based deep convolution neural network model, 4-category classification of pathological images were performed to diagnose the non-inflammatory aortic lesion. Results: The prediction model of artificial intelligence assisted diagnosis had the best accuracy, sensitivity and precision in identifying lesions with smooth muscle cell nuclei loss, which were 99.39%, 98.36% and 98.36%, respectively. The classification accuracy of elastic fiber fragmentation and/or loss lesions was 98.08%, while that of intralamellar mucoid extracellular matrix accumulation lesions was 96.93%. The overall accuracy of the classification model was 96.32%, and the area under the curve was 0.982. Conclusions: The accuracy of deep learning neural network model in the 4-category classification of non-inflammatory aortic lesionsis confirmed based on digital photomicrographs. This method can effectively improve the diagnostic efficiency of pathologists.


Assuntos
Aprendizado Profundo , Inteligência Artificial , China , Hematoxilina , Redes Neurais de Computação
12.
Rev Med Liege ; 76(5-6): 344-351, 2021 May.
Artigo em Francês | MEDLINE | ID: mdl-34080361

RESUMO

Oncological imaging is a subspecialty of medical imaging and focuses on the workup and the follow-up of cancer. Oncological imaging takes into account all the specificities of cancer diseases, which is a constantly evolving field, especially in the era of precision medicine, and plays a key role in the care of cancer patients. It permits reliable diagnosis and gives precious information concerning disease extension at diagnosis, which is essential for the treatment planning. Oncological imaging allows also followup of patients under treatment, using response evaluation scores. Interventional imaging, which provides minimally invasive procedures, is useful in order to obtain a histological diagnosis, to treat some tumour or to improve quality of life of cancer patients. Finally, numerous perspectives, among them the advent of artificial intelligence (radiomics), will further strengthen the role of oncologic imaging in the near future.


Assuntos
Inteligência Artificial , Neoplasias , Diagnóstico por Imagem , Seguimentos , Humanos , Neoplasias/diagnóstico por imagem , Qualidade de Vida
13.
Rev Med Liege ; 76(5-6): 358-361, 2021 May.
Artigo em Francês | MEDLINE | ID: mdl-34080363

RESUMO

The anatomo-pathological diagnosis of tumors is based on many criteria related mainly to image analysis. Currently, in most pathology laboratories, tissues or cells are placed on glass slides and directly analyzed with an optical microscope. Because of technological evolutions, it is currently possible to digitize slides (digital pathology). The digitization of whole slides has allowed the development of computer programs of artificial intelligence (AI) for image analysis. Applied to tumour pathology, this technology allows the detection, diagnosis or evaluation of the prognosis of neoplastic lesions. There are many challenges associated with the use of AI in routine pathology. These are mainly related to the amount of data to be analyzed and to the development of reliable algorithms. Nevertheless, this technology is promising and could become a valuable aid in the field of precision medicine for which the amount of data related to a patient is constantly increasing.


Assuntos
Inteligência Artificial , Neoplasias , Algoritmos , Humanos , Processamento de Imagem Assistida por Computador , Microscopia , Neoplasias/diagnóstico
14.
Rev Med Liege ; 76(5-6): 369-374, 2021 May.
Artigo em Francês | MEDLINE | ID: mdl-34080365

RESUMO

Cancer incidence is steadily progressing worldwide, in parallel with the aging of the population. Workload is increasing constantly, especially in the fields of oncology and radiotherapy. This is particularly worrysome, as there is a general shortage of skilled professionals in the field (for example in medical physics). Moreover, every single patient does represent an enormous amount of data issued from a wide range of sources. This is especially true as far a medical imaging is concerned. Extraction of morphological data (anatomical location and extent of the tumour) and functional data (tumour biology and metabolism in general) becomes laborious. Moreover, images contain information which cannot be discerned by the human eye. Therefore, to handle shortage of human resources and transform this enormous amount of data automatically, artificial intelligence becomes a «must have¼. We intend to highlight the growing importance of radiomics as a cornerstone in automation of processes in radiotherapy, especially for treatment planification and a more personalized individualized treatment approach.


Assuntos
Neoplasias , Radioterapia (Especialidade) , Inteligência Artificial , Automação , Diagnóstico por Imagem , Humanos , Neoplasias/diagnóstico por imagem , Neoplasias/radioterapia
15.
Artigo em Inglês | MEDLINE | ID: mdl-34068530

RESUMO

The COVID-19 pandemic has altered healthcare delivery platforms from traditional face-to-face formats to online care through digital tools. The healthcare industry saw a rapid adoption of digital collaborative tools to provide care to patients, regardless of where patients or clinicians were located, while mitigating the risk of exposure to the coronavirus. Information technologies now allow healthcare providers to continue a high level of care for their patients through virtual visits, and to collaborate with other providers in the networks. Population health can be improved by social determinants of health and precision medicine working together. However, these two health-enhancing constructs work independently, resulting in suboptimal health results. This paper argues that artificial intelligence can provide clinical-community linkage that enhances overall population health. An exploratory roadmap is proposed.


Assuntos
Inteligência Artificial , COVID-19 , Humanos , Pandemias , Medicina de Precisão , SARS-CoV-2 , Determinantes Sociais da Saúde
16.
Sheng Wu Gong Cheng Xue Bao ; 37(5): 1564-1577, 2021 May 25.
Artigo em Chinês | MEDLINE | ID: mdl-34085443

RESUMO

As an important model industrial microorganism, Escherichia coli has been widely used in pharmaceutical, chemical industry and agriculture. In the past 30 years, a variety of new strategies and techniques, including artificial intelligence, gene editing, metabolic pathway assembly, and dynamic regulation have been used to design, construct, and optimize E. coli cell factories, which remarkably improved the efficiency for biotechnological production of chemicals. In this review, three key aspects for constructing E. coli cell factories, including pathway design, pathway assembly and regulation, and optimization of global cellular performance, are summarized. The technologies that have played important roles in metabolic engineering of E. coli, as well as their future applications, are discussed.


Assuntos
Inteligência Artificial , Escherichia coli , Escherichia coli/genética , Edição de Genes , Engenharia Metabólica , Redes e Vias Metabólicas/genética
17.
Am J Orthod Dentofacial Orthop ; 159(6): 824-835.e1, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34059213

RESUMO

INTRODUCTION: This study aimed to test the accuracy of a new automatic deep learning-based approach on the basis of convolutional neural networks (CNN) for fully automatic segmentation of the sinonasal cavity and the pharyngeal airway from cone-beam computed tomography (CBCT) scans. METHODS: Forty CBCT scans from healthy patients (20 women and 20 men; mean age, 23.37 ± 3.34 years) were collected, and manual segmentation of the sinonasal cavity and pharyngeal subregions were carried out by using Mimics software (version 20.0; Materialise, Leuven, Belgium). Twenty CBCT scans from the total sample were randomly selected and used for training the artificial intelligence model file. The remaining 20 CBCT segmentation masks were used to test the accuracy of the CNN fully automatic method by comparing the segmentation volumes of the 3-dimensional models obtained with automatic and manual segmentations. The accuracy of the CNN-based method was also assessed by using the Dice score coefficient and by the surface-to-surface matching technique. The intraclass correlation coefficient and Dahlberg's formula were used to test the intraobserver reliability and method error, respectively. Independent Student t test was used for between-groups volumetric comparison. RESULTS: Measurements were highly correlated with an intraclass correlation coefficient value of 0.921, whereas the method error was 0.31 mm3. A mean difference of 1.93 ± 0.73 cm3 was found between the methodologies, but it was not statistically significant (P >0.05). The mean matching percentage detected was 85.35 ± 2.59 (tolerance 0.5 mm) and 93.44 ± 2.54 (tolerance 1.0 mm). The differences, measured as the Dice score coefficient in percentage, between the assessments done with both methods were 3.3% and 5.8%, respectively. CONCLUSIONS: The new deep learning-based method for automated segmentation of the sinonasal cavity and the pharyngeal airway in CBCT scans is accurate and performs equally well as an experienced image reader.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Adulto , Tomografia Computadorizada de Feixe Cônico , Feminino , Humanos , Masculino , Faringe/diagnóstico por imagem , Reprodutibilidade dos Testes , Adulto Jovem
18.
BMC Public Health ; 21(1): 1065, 2021 06 04.
Artigo em Inglês | MEDLINE | ID: mdl-34088286

RESUMO

BACKGROUND: Population-based screening was essential for glaucoma management. Although various studies have investigated the cost-effectiveness of glaucoma screening, policymakers facing with uncontrollably growing total health expenses were deeply concerned about the potential financial consequences of glaucoma screening. This present study was aimed to explore the impact of glaucoma screening with artificial intelligence (AI) automated diagnosis from a budgetary standpoint in Changjiang county, China. METHODS: A Markov model based on health care system's perspective was adapted from previously published studies to predict disease progression and healthcare costs. A cohort of 19,395 individuals aged 65 and above were simulated over a 15-year timeframe. Fur illustrative purpose, we only considered primary angle-closure glaucoma (PACG) in this study. Prevalence, disease progression risks between stages, compliance rates were obtained from publish studies. We did a meta-analysis to estimate diagnostic performance of AI automated diagnosis system from fundus image. Screening costs were provided by the Changjiang screening programme, whereas treatment costs were derived from electronic medical records from two county hospitals. Main outcomes included the number of PACG patients and health care costs. Cost-offset analysis was employed to compare projected health outcomes and medical care costs under the screening with what they would have been without screening. One-way sensitivity analysis was conducted to quantify uncertainties around model results. RESULTS: Among people aged 65 and above in Changjiang county, it was predicted that there were 1940 PACG patients under the AI-assisted screening scenario, compared with 2104 patients without screening in 15 years' time. Specifically, the screening would reduce patients with primary angle closure suspect by 7.7%, primary angle closure by 8.8%, PACG by 16.7%, and visual blindness by 33.3%. Due to early diagnosis and treatment under the screening, healthcare costs surged dramatically to $107,761.4 dollar in the first year and then were constantly declining over time, while without screening costs grew from $14,759.8 in the second year until peaking at $17,900.9 in the 9th year. However, cost-offset analysis revealed that additional healthcare costs resulted from the screening could not be offset by decreased disease progression. The 5-, 10-, and 15-year accumulated incremental costs of screening versus no screening were estimated to be $396,362.8, $424,907.9, and $434,903.2, respectively. As a result, the incremental cost per PACG of any stages prevented was $1464.3. CONCLUSIONS: This study represented the first attempt to address decision-maker's budgetary concerns when adopting glaucoma screening by developing a Markov prediction model to project health outcomes and costs. Population screening combined with AI automated diagnosis for PACG in China were able to reduce disease progression risks. However, the excess costs of screening could never be offset by reduction in disease progression. Further studies examining the cost-effectiveness or cost-utility of AI-assisted glaucoma screening were needed.


Assuntos
Glaucoma de Ângulo Fechado , Glaucoma , Idoso , Inteligência Artificial , China/epidemiologia , Análise Custo-Benefício , Glaucoma/diagnóstico , Custos de Cuidados de Saúde , Humanos
19.
World J Gastroenterol ; 27(21): 2681-2709, 2021 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-34135549

RESUMO

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN's clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.


Assuntos
Inteligência Artificial , Gastroenterologistas , Humanos , Redes Neurais de Computação , Prognóstico
20.
World J Gastroenterol ; 27(21): 2758-2770, 2021 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-34135552

RESUMO

Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called "big data" to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.


Assuntos
Procedimentos Cirúrgicos do Sistema Digestório , Cirurgiões , Algoritmos , Inteligência Artificial , Big Data , Procedimentos Cirúrgicos do Sistema Digestório/efeitos adversos , Humanos
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