Artificial intelligence-based decision-making for age-related macular degeneration

Hwang, De‐Kuang; Hsu, Chih Chien; Chang, Kao-Jung; Chao, Daniel L.; Sun, Chuan; Jheng, Ying Chun; Yarmishyn, Aliaksandr A.; Wu, Jau Ching; Tsai, Ching Yao; Wang, Mong‐Lien; Peng, Chi Hsien; Chien, Ke Hung; Kao, Chung Lan; Lin, Tzu‐Chin; Woung, Lin‐Chung; Chen, Shih‐Jen; Chiou, Shih Hwa

doi:10.7150/thno.28447

Cited by 140 publications

(87 citation statements)

References 17 publications

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“…Accepting that abstracts are usually word limited, even in the discussion sections of the main text, nearly two thirds of studies failed to make an explicit recommendation for further prospective studies or trials. One retrospective study gave a website address in the abstract for patients to upload their eye scans and use the algorithm themselves 33. Overpromising language leaves studies vulnerable to being misinterpreted by the media and the public.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

Nagendran

Chen

Lovejoy³

et al. 2020

BMJ

681

500

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Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. Conclusions Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. Study registration PROSPERO CRD42019123605.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

Nagendran

Chen

Lovejoy³

et al. 2020

BMJ

681

500

View full text Add to dashboard Cite

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“…6 , 7 Moreover, the VGG16 and InceptionV3 have been enormously applied to assist medical image identification, such as taxonomy of the CT image with lung cancer to classify the pathological types, 8 to identify the endoscopy images with different lesions, 9 and assisted ophthalmologist to analyze the variation of the OCT images. 10 , 11 …”

Section: Discussionmentioning

confidence: 99%

Optical coherence tomography–based diabetic macula edema screening with artificial intelligence

Hwang

Chou

Lin

et al. 2020

Journal of the Chinese Medical Association

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Background: Optical coherence tomography (OCT) is considered as a sensitive and noninvasive tool to evaluate the macular lesions. In patients with diabetes mellitus (DM), the existence of diabetic macular edema (DME) can cause significant vision impairment and further intravitreal injection (IVI) of anti–vascular endothelial growth factor (VEGF) is needed. However, the increasing number of DM patients makes it a big burden for clinicians to manually determine whether DME exists in the OCT images. The artificial intelligence (AI) now enormously applied to many medical territories may help reduce the burden on clinicians. Methods: We selected DME patients receiving IVI of anti-VEGF or corticosteroid at Taipei Veterans General Hospital in 2017. All macular cross-sectional scan OCT images were collected retrospectively from the eyes of these patients from January 2008 to July 2018. We further established AI models based on convolutional neural network architecture to determine whether the DM patients have DME by OCT images. Results: Based on the convolutional neural networks, InceptionV3 and VGG16, our AI system achieved a high DME diagnostic accuracy of 93.09% and 92.82%, respectively. The sensitivity of the VGG16 and InceptionV3 models was 96.48% and 95.15%., respectively. The specificity was corresponding to 86.67% and 89.63% for VGG16 and InceptionV3, respectively. We further developed an OCT-driven platform based on these AI models. Conclusion: We successfully set up AI models to provide an accurate diagnosis of DME by OCT images. These models may assist clinicians in screening DME in DM patients in the future.

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“…For accelerating telehealth services implementation for AMD, such as remote consultations with specialists, or consumer home monitoring, it was suggested to facilitate the combination of nonmydriatic fundus cameras and technologies such as OCT and OCT angiography [ 48 ]. Notably, a dedicated AI- and cloud-based approach based on convolutional neural networks introduced by Hwang et al achieved equivalent diagnostic accuracy as that of a retinal specialist examination [ 49 ].…”

Section: Disorders Of the Retinamentioning

confidence: 99%

Telemedicine in ophthalmology in view of the emerging COVID-19 outbreak

Sommer

Blumenthal

2020

Graefes Arch Clin Exp Ophthalmol

102

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Purpose Technological advances in recent years have resulted in the development and implementation of various modalities and techniques enabling medical professionals to remotely diagnose and treat numerous medical conditions in diverse medical fields, including ophthalmology. Patients who require prolonged isolation until recovery, such as those who suffer from COVID-19, present multiple therapeutic dilemmas to their caregivers. Therefore, utilizing remote care in the daily workflow would be a valuable tool for the diagnosis and treatment of acute and chronic ocular conditions in this challenging clinical setting. Our aim is to review the latest technological and methodical advances in teleophthalmology and highlight their implementation in screening and managing various ocular conditions. We present them as well as potential diagnostic and treatment applications in view of the recent SARS-CoV-2 virus outbreak. Methods A computerized search from January 2017 up to March 2020 of the online electronic database PubMed was performed, using the following search strings: "telemedicine," "telehealth," and "ophthalmology." More generalized complementary contemporary research data regarding the COVID-19 pandemic was also obtained from the PubMed database. Results A total of 312 records, including COVID-19-focused studies, were initially identified. After exclusion of nonrelevant, non-English, and duplicate studies, a total of 138 records were found eligible. Ninety records were included in the final qualitative analysis. Conclusion Teleophthalmology is an effective screening and management tool for a range of adult and pediatric acute and chronic ocular conditions. It is mostly utilized in screening of retinal conditions such as retinopathy of prematurity, diabetic retinopathy, and age-related macular degeneration; in diagnosing anterior segment condition; and in managing glaucoma. With improvements in image processing, and better integration of the patient's medical record, teleophthalmology should become a more accepted modality, all the more so in circumstances where social distancing is inflicted upon us.

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Artificial intelligence-based decision-making for age-related macular degeneration

Cited by 140 publications

References 17 publications

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies

Optical coherence tomography–based diabetic macula edema screening with artificial intelligence

Telemedicine in ophthalmology in view of the emerging COVID-19 outbreak

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