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Venkatesh R, Gandhi P, Choudhary A, Kathare R, Chhablani J, Prabhu V, Bavaskar S, Hande P, Shetty R, Reddy NG, Rani PK, Yadav NK. Evaluation of Systemic Risk Factors in Patients with Diabetes Mellitus for Detecting Diabetic Retinopathy with Random Forest Classification Model. Diagnostics (Basel) 2024; 14:1765. [PMID: 39202252 PMCID: PMC11353512 DOI: 10.3390/diagnostics14161765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Revised: 08/08/2024] [Accepted: 08/12/2024] [Indexed: 09/03/2024] Open
Abstract
BACKGROUND This study aims to assess systemic risk factors in diabetes mellitus (DM) patients and predict diabetic retinopathy (DR) using a Random Forest (RF) classification model. METHODS We included DM patients presenting to the retina clinic for first-time DR screening. Data on age, gender, diabetes type, treatment history, DM control status, family history, pregnancy history, and systemic comorbidities were collected. DR and sight-threatening DR (STDR) were diagnosed via a dilated fundus examination. The dataset was split 80:20 into training and testing sets. The RF model was trained to detect DR and STDR separately, and its performance was evaluated using misclassification rates, sensitivity, and specificity. RESULTS Data from 1416 DM patients were analyzed. The RF model was trained on 1132 (80%) patients. The misclassification rates were 0% for DR and ~20% for STDR in the training set. External testing on 284 (20%) patients showed 100% accuracy, sensitivity, and specificity for DR detection. For STDR, the model achieved 76% (95% CI-70.7%-80.7%) accuracy, 53% (95% CI-39.2%-66.6%) sensitivity, and 80% (95% CI-74.6%-84.7%) specificity. CONCLUSIONS The RF model effectively predicts DR in DM patients using systemic risk factors, potentially reducing unnecessary referrals for DR screening. However, further validation with diverse datasets is necessary to establish its reliability for clinical use.
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Affiliation(s)
- Ramesh Venkatesh
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Priyanka Gandhi
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Ayushi Choudhary
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Rupal Kathare
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Jay Chhablani
- Medical Retina and Vitreoretinal Surgery, University of Pittsburgh School of Medicine, Pittsburg, PA 15213, USA;
| | - Vishma Prabhu
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Snehal Bavaskar
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Prathiba Hande
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
| | - Rohit Shetty
- Department of Cornea and Refractive Services, Narayana Nethralaya, Bengaluru 560010, India;
| | - Nikitha Gurram Reddy
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Kallam Anji Reddy Campus, Hyderabad 500034, India; (N.G.R.); (P.K.R.)
| | - Padmaja Kumari Rani
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Kallam Anji Reddy Campus, Hyderabad 500034, India; (N.G.R.); (P.K.R.)
| | - Naresh Kumar Yadav
- Department of Retina and Vitreous, Narayana Nethralaya, Bengaluru 560010, India; (P.G.); (A.C.); (R.K.); (V.P.); (S.B.); (P.H.); (N.K.Y.)
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Kong M, Song SJ. Artificial Intelligence Applications in Diabetic Retinopathy: What We Have Now and What to Expect in the Future. Endocrinol Metab (Seoul) 2024; 39:416-424. [PMID: 38853435 PMCID: PMC11220221 DOI: 10.3803/enm.2023.1913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/11/2024] [Accepted: 04/03/2024] [Indexed: 06/11/2024] Open
Abstract
Diabetic retinopathy (DR) is a major complication of diabetes mellitus and is a leading cause of vision loss globally. A prompt and accurate diagnosis is crucial for ensuring favorable visual outcomes, highlighting the need for increased access to medical care. The recent remarkable advancements in artificial intelligence (AI) have raised high expectations for its role in disease diagnosis and prognosis prediction across various medical fields. In addition to achieving high precision comparable to that of ophthalmologists, AI-based diagnosis of DR has the potential to improve medical accessibility, especially through telemedicine. In this review paper, we aim to examine the current role of AI in the diagnosis of DR and explore future directions.
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Affiliation(s)
- Mingui Kong
- Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Su Jeong Song
- Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea
- Biomedical Institute for Convergence (BICS), Sungkyunkwan University, Suwon, Korea
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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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