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Gopalakrishnan N, Joshi A, Chhablani J, Yadav NK, Reddy NG, Rani PK, Pulipaka RS, Shetty R, Sinha S, Prabhu V, Venkatesh R. Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios. Int J Retina Vitreous 2024; 10:11. [PMID: 38268046 PMCID: PMC10809735 DOI: 10.1186/s40942-024-00533-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/19/2024] [Indexed: 01/26/2024] Open
Abstract
PURPOSE To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases. METHODS Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the "majority clinician response" (defined as the maximum number of identical responses provided by the clinicians) and "majority AI-platform" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned. RESULTS Clinicians, AI platforms, and the "majority clinician response" and "majority AI response" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately. CONCLUSION Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations. CLINICAL TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Nikhil Gopalakrishnan
- Department of Retina and Vitreous, Narayana Nethralaya Eye Hospital, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India
| | - Aishwarya Joshi
- Department of Retina and Vitreous, Narayana Nethralaya Eye Hospital, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India
| | - Jay Chhablani
- Medical Retina and Vitreoretinal Surgery, University of Pittsburgh School of Medicine, 203 Lothrop Street, Suite 800, Pittsburg, PA, 15213, USA
| | - Naresh Kumar Yadav
- Department of Retina and Vitreous, Narayana Nethralaya Eye Hospital, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India
| | - Nikitha Gurram Reddy
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Kallam Anji Reddy Campus, Hyderabad, Telangana, 500034, India
| | - Padmaja Kumari Rani
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Kallam Anji Reddy Campus, Hyderabad, Telangana, 500034, India
| | - Ram Snehith Pulipaka
- Prime Retina Eye Care Center, 3-6-106/1, Street Number 19, Opposite to Vijaya Diagnostic Centre, Himayatnagar, Hyderabad, Telangana, 500029, India
| | - Rohit Shetty
- Department of Cornea and Refractive Services, Narayana Nethralaya Eye Hospital, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India
| | - Shivani Sinha
- Department of Vitreo-Retina, Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Sheikhpura, Patna, Bihar, 800014, India
| | - Vishma Prabhu
- Department of Retina and Vitreous, Narayana Nethralaya Eye Hospital, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India
| | - Ramesh Venkatesh
- Department of Retina and Vitreous, Narayana Nethralaya Eye Hospital, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, Karnataka, 560010, India.
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Alabdulwahhab KM. Diabetic Retinopathy Screening Using Non-Mydriatic Fundus Camera in Primary Health Care Settings - A Multicenter Study from Saudi Arabia. Int J Gen Med 2023; 16:2255-2262. [PMID: 37304902 PMCID: PMC10255608 DOI: 10.2147/ijgm.s410197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 05/30/2023] [Indexed: 06/13/2023] Open
Abstract
Background Screening of diabetic retinopathy (DR) using the current digital imaging facilities in a primary health care setting is still in its early stages in Saudi Arabia. This study aims to reduce the risk of vision impairment and blindness among known diabetic people through early identification by general practitioners (GP) in a primary health care setting in Saudi Arabia. The objective of this study was to evaluate the accuracy of diabetic retinopathy (DR) detection by general practitioners (GPs) by comparing the agreement of DR assessment between GPs and ophthalmologists' assessment as a gold standard. Methods A hospital-based, six-month cross-sectional study was conducted, and the participants were type 2 diabetic adults from the diabetic registries of seven rural PHCs, in Saudi Arabia. After medical examination, the participants were then evaluated by fundus photography using a non-mydriatic fundus camera without medication for mydriasis. Presence or absence of DR was graded by the trained GPs in the PHCs and then compared with the grading of an ophthalmologist which was taken as a reference or a gold standard. Results A total of 899 diabetic patients were included, and the mean age of the patients was 64.89 ± 11.01 years. The evaluation by the GPs had a sensitivity of 80.69 [95% CI 74.8-85.4]; specificity of 92.23 [88.7-96.3]; positive predictive value, 74.1 [70.4-77.0]; negative predictive value, 73.34 [70.6-77.9]; and an accuracy of 84.57 [81.8-89.88]. For the consensus of agreement the adjusted kappa coefficient was from 0.74 to 0.92 for the DR. Conclusion This study demonstrates that trained GPs in rural health centers are able to provide reliable detection results of DR from fundus photographs. The study highlights the need for early DR screening programs in the rural areas of Saudi Arabia to facilitate early identification of the condition and to lessen impact of blindness due to diabetes.
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Chemello G, Salvatori B, Morettini M, Tura A. Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review. BIOSENSORS 2022; 12:985. [PMID: 36354494 PMCID: PMC9688674 DOI: 10.3390/bios12110985] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/26/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.
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Affiliation(s)
- Gaetano Chemello
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
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Ferro Desideri L, Rutigliani C, Corazza P, Nastasi A, Roda M, Nicolo M, Traverso CE, Vagge A. The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases. JOURNAL OF OPTOMETRY 2022; 15 Suppl 1:S50-S57. [PMID: 36216736 PMCID: PMC9732476 DOI: 10.1016/j.optom.2022.08.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/14/2022] [Accepted: 08/16/2022] [Indexed: 06/16/2023]
Abstract
In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice.
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Affiliation(s)
- Lorenzo Ferro Desideri
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy.
| | | | - Paolo Corazza
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | | | - Matilde Roda
- Ophthalmology Unit, Department of Experimental, Diagnostic and Specialty Medicine (DIMES), Alma Mater Studiorum University of Bologna and S.Orsola-Malpighi Teaching Hospital, Bologna, Italy
| | - Massimo Nicolo
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Carlo Enrico Traverso
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
| | - Aldo Vagge
- University Eye Clinic of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI), University of Genoa, Italy
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Zhang WF, Li DH, Wei QJ, Ding DY, Meng LH, Wang YL, Zhao XY, Chen YX. The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy. Front Med (Lausanne) 2022; 9:839088. [PMID: 35652075 PMCID: PMC9148973 DOI: 10.3389/fmed.2022.839088] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 04/08/2022] [Indexed: 12/26/2022] Open
Abstract
Purpose To evaluate the performance of a deep learning (DL)-based artificial intelligence (AI) hierarchical diagnosis software, EyeWisdom V1 for diabetic retinopathy (DR). Materials and Methods The prospective study was a multicenter, double-blind, and self-controlled clinical trial. Non-dilated posterior pole fundus images were evaluated by ophthalmologists and EyeWisdom V1, respectively. The diagnosis of manual grading was considered as the gold standard. Primary evaluation index (sensitivity and specificity) and secondary evaluation index like positive predictive values (PPV), negative predictive values (NPV), etc., were calculated to evaluate the performance of EyeWisdom V1. Results A total of 1,089 fundus images from 630 patients were included, with a mean age of (56.52 ± 11.13) years. For any DR, the sensitivity, specificity, PPV, and NPV were 98.23% (95% CI 96.93-99.08%), 74.45% (95% CI 69.95-78.60%), 86.38% (95% CI 83.76-88.72%), and 96.23% (95% CI 93.50-98.04%), respectively; For sight-threatening DR (STDR, severe non-proliferative DR or worse), the above indicators were 80.47% (95% CI 75.07-85.14%), 97.96% (95% CI 96.75-98.81%), 92.38% (95% CI 88.07-95.50%), and 94.23% (95% CI 92.46-95.68%); For referral DR (moderate non-proliferative DR or worse), the sensitivity and specificity were 92.96% (95% CI 90.66-94.84%) and 93.32% (95% CI 90.65-95.42%), with the PPV of 94.93% (95% CI 92.89-96.53%) and the NPV of 90.78% (95% CI 87.81-93.22%). The kappa score of EyeWisdom V1 was 0.860 (0.827-0.890) with the AUC of 0.958 for referral DR. Conclusion The EyeWisdom V1 could provide reliable DR grading and referral recommendation based on the fundus images of diabetics.
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Affiliation(s)
- Wen-fei Zhang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | | | - Qi-jie Wei
- Visionary Intelligence Ltd., Beijing, China
| | | | - Li-hui Meng
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yue-lin Wang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xin-yu Zhao
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - You-xin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Wang R, Zuo G, Li K, Li W, Xuan Z, Han Y, Yang W. Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy. Front Endocrinol (Lausanne) 2022; 13:1036426. [PMID: 36387891 PMCID: PMC9659570 DOI: 10.3389/fendo.2022.1036426] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 10/17/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI's general applications and research frontiers in the detection and gradation of DR. METHODS Citation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software. RESULTS Overall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled "automated detection" was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning. CONCLUSION Initial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.
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Affiliation(s)
- Ruoyu Wang
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Guangxi Zuo
- The First School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Wangting Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Zhiqiang Xuan
- Institute of Occupational Health and Radiation Protection, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
- *Correspondence: Zhiqiang Xuan, ; Yongzhao Han, ; Weihua Yang,
| | - Yongzhao Han
- Affiliated Jiangning Hospital, Nanjing Medical University, Nanjing, China
- *Correspondence: Zhiqiang Xuan, ; Yongzhao Han, ; Weihua Yang,
| | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
- *Correspondence: Zhiqiang Xuan, ; Yongzhao Han, ; Weihua Yang,
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Ursin F, Timmermann C, Orzechowski M, Steger F. Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent? Front Med (Lausanne) 2021; 8:695217. [PMID: 34368192 PMCID: PMC8333706 DOI: 10.3389/fmed.2021.695217] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/22/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure. Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI. Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.
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Artificial Intelligence-Based Diagnosis of Diabetes Mellitus: Combining Fundus Photography with Traditional Chinese Medicine Diagnostic Methodology. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5556057. [PMID: 33969117 PMCID: PMC8081616 DOI: 10.1155/2021/5556057] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/02/2021] [Accepted: 04/05/2021] [Indexed: 12/12/2022]
Abstract
In this study, we propose a technique for diagnosing both type 1 and type 2 diabetes in a quick, noninvasive way by using equipment that is easy to transport. Diabetes mellitus is a chronic disease that affects public health globally. Although diabetes mellitus can be accurately diagnosed using conventional methods, these methods require the collection of data in a clinical setting and are unlikely to be feasible in areas with few medical resources. This technique combines an analysis of fundus photography of the physical and physiological features of the patient, namely, the tongue and the pulse, which are used in Traditional Chinese Medicine. A random forest algorithm was used to analyze the data, and the accuracy, precision, recall, and F1 scores for the correct classification of diabetes were 0.85, 0.89, 0.67, and 0.76, respectively. The proposed technique for diabetes diagnosis offers a new approach to the diagnosis of diabetes, in that it may be convenient in regions that lack medical resources, where the early detection of diabetes is difficult to achieve.
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