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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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Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmol Ther 2024; 13:2125-2149. [PMID: 38913289 PMCID: PMC11246322 DOI: 10.1007/s40123-024-00981-4] [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: 02/19/2024] [Accepted: 04/15/2024] [Indexed: 06/25/2024] Open
Abstract
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań , Poland.
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingxin Zhou
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Meizhu Wang
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Tien Y Wong
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
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Richardson A, Kundu A, Henao R, Lee T, Scott BL, Grewal DS, Fekrat S. Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network. Transl Vis Sci Technol 2024; 13:23. [PMID: 39136960 PMCID: PMC11323992 DOI: 10.1167/tvst.13.8.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/23/2024] [Indexed: 08/16/2024] Open
Abstract
Purpose Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group. Methods We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values. Results In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Conclusions Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. Translational Relevance Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.
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Affiliation(s)
- Alexander Richardson
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Anita Kundu
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
| | - Ricardo Henao
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Terry Lee
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
| | - Burton L. Scott
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Dilraj S. Grewal
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
| | - Sharon Fekrat
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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Yao J, Lim J, Lim GYS, Ong JCL, Ke Y, Tan TF, Tan TE, Vujosevic S, Ting DSW. Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema. EYE AND VISION (LONDON, ENGLAND) 2024; 11:23. [PMID: 38880890 PMCID: PMC11181581 DOI: 10.1186/s40662-024-00389-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/09/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. MAIN TEXT This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. CONCLUSION AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
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Affiliation(s)
- Jie Yao
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Joshua Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Gilbert Yong San Lim
- Duke-NUS Medical School, Singapore, Singapore
- SingHealth AI Health Program, Singapore, Singapore
| | - Jasmine Chiat Ling Ong
- Duke-NUS Medical School, Singapore, Singapore
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore
| | - Yuhe Ke
- Department of Anesthesiology and Perioperative Science, Singapore General Hospital, Singapore, Singapore
| | - Ting Fang Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Eye Clinic, IRCCS MultiMedica, Milan, Italy
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- SingHealth AI Health Program, Singapore, Singapore.
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Cai Y, Zhang X, Cao J, Grzybowski A, Ye J, Lou L. Application of artificial intelligence in oculoplastics. Clin Dermatol 2024; 42:259-267. [PMID: 38184122 DOI: 10.1016/j.clindermatol.2023.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2024]
Abstract
Oculoplastics is a subspecialty of ophthalmology/dermatology concerned with eyelid, orbital, and lacrimal diseases. Artificial intelligence (AI), with its powerful ability to analyze large data sets, has dramatically benefited oculoplastics. The cutting-edge AI technology is widely applied to extract ocular parameters and to use these results for further assessment, such as screening and diagnosis of blepharoptosis and predicting the progression of thyroid eye disease. AI also assists in treatment procedures, such as surgical strategy planning in blepharoptosis. High efficiency and high reliability are the most apparent advantages of AI, with promising prospects. The possibilities of AI in oculoplastics may lie in three-dimensional modeling technology and image generation. We retrospectively summarize AI applications involving eyelid, orbital, and lacrimal diseases in oculoplastics, and we also examine the strengths and weaknesses of AI technology in oculoplastics.
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Affiliation(s)
- Yilu Cai
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Xuan Zhang
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Jing Cao
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Lixia Lou
- Eye Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China.
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Strzelecki M, Kociołek M, Strąkowska M, Kozłowski M, Grzybowski A, Szczypiński PM. Artificial intelligence in the detection of skin cancer: State of the art. Clin Dermatol 2024; 42:280-295. [PMID: 38181888 DOI: 10.1016/j.clindermatol.2023.12.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024]
Abstract
The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applications has recently undergone dynamic development, abounding in several solutions that are effective tools supporting diagnosticians in many medical specialties. In this contribution, a number of applications of different classes of AI algorithms for the detection of this skin melanoma are presented and evaluated. Both classic systems based on the analysis of dermatoscopic images as well as total body systems, enabling the analysis of the patient's whole body to detect moles and pathologic changes, are discussed. These increasingly popular applications that allow the analysis of lesion images using smartphones are also described. The quantitative evaluation of the discussed systems with particular emphasis on the method of validation of the implemented algorithms is presented. The advantages and limitations of AI in the analysis of lesion images are also discussed, and problems requiring a solution for more effective use of AI in dermatology are identified.
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Affiliation(s)
- Michał Strzelecki
- Institute of Electronics, Lodz University of Technology, Łódź, Poland.
| | - Marcin Kociołek
- Institute of Electronics, Lodz University of Technology, Łódź, Poland
| | - Maria Strąkowska
- Institute of Electronics, Lodz University of Technology, Łódź, Poland
| | - Michał Kozłowski
- Department of Mechatronics and Technical and IT Education, Faculty of Technical Science, University of Warmia and Mazury, Olsztyn, Poland
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
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Cicinelli MV, Gravina S, Rutigliani C, Checchin L, La Franca L, Lattanzio R, Bandello F. Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging. Transl Vis Sci Technol 2024; 13:11. [PMID: 38488432 PMCID: PMC10946690 DOI: 10.1167/tvst.13.3.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 02/04/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. Methods A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. Results The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity = 0.83, specificity = 0.95 for proliferative DR) and had the highest agreement with the GT (wκ = 0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity = 0.33, specificity = 0.98 for proliferative DR; wκ = 0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P < 0.001). Conclusions Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. Translational Relevance This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.
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Affiliation(s)
- Maria Vittoria Cicinelli
- Department of Ophthalmology, IRCCS San Raffaele Hospital, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Salvatore Gravina
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Carola Rutigliani
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
| | - Lisa Checchin
- Department of Ophthalmology, IRCCS San Raffaele Hospital, Milan, Italy
| | | | | | - Francesco Bandello
- Department of Ophthalmology, IRCCS San Raffaele Hospital, Milan, Italy
- School of Medicine, Vita-Salute San Raffaele University, Milan, Italy
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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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Rajesh AE, Davidson OQ, Lee CS, Lee AY. Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness. Diabetes Care 2023; 46:1728-1739. [PMID: 37729502 PMCID: PMC10516248 DOI: 10.2337/dci23-0032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/15/2023] [Indexed: 09/22/2023]
Abstract
Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.
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Affiliation(s)
- Anand E. Rajesh
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Oliver Q. Davidson
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA
- Roger H. and Angie Karalis Johnson Retina Center, Seattle, WA
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Li Y, El Habib Daho M, Conze PH, Zeghlache R, Le Boité H, Bonnin S, Cosette D, Magazzeni S, Lay B, Le Guilcher A, Tadayoni R, Cochener B, Lamard M, Quellec G. Hybrid Fusion of High-Resolution and Ultra-Widefield OCTA Acquisitions for the Automatic Diagnosis of Diabetic Retinopathy. Diagnostics (Basel) 2023; 13:2770. [PMID: 37685306 PMCID: PMC10486731 DOI: 10.3390/diagnostics13172770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Optical coherence tomography angiography (OCTA) can deliver enhanced diagnosis for diabetic retinopathy (DR). This study evaluated a deep learning (DL) algorithm for automatic DR severity assessment using high-resolution and ultra-widefield (UWF) OCTA. Diabetic patients were examined with 6×6 mm2 high-resolution OCTA and 15×15 mm2 UWF-OCTA using PLEX®Elite 9000. A novel DL algorithm was trained for automatic DR severity inference using both OCTA acquisitions. The algorithm employed a unique hybrid fusion framework, integrating structural and flow information from both acquisitions. It was trained on data from 875 eyes of 444 patients. Tested on 53 patients (97 eyes), the algorithm achieved a good area under the receiver operating characteristic curve (AUC) for detecting DR (0.8868), moderate non-proliferative DR (0.8276), severe non-proliferative DR (0.8376), and proliferative/treated DR (0.9070). These results significantly outperformed detection with the 6×6 mm2 (AUC = 0.8462, 0.7793, 0.7889, and 0.8104, respectively) or 15×15 mm2 (AUC = 0.8251, 0.7745, 0.7967, and 0.8786, respectively) acquisitions alone. Thus, combining high-resolution and UWF-OCTA acquisitions holds the potential for improved early and late-stage DR detection, offering a foundation for enhancing DR management and a clear path for future works involving expanded datasets and integrating additional imaging modalities.
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Affiliation(s)
- Yihao Li
- Inserm, UMR 1101 LaTIM, F-29200 Brest, France
- Univ Bretagne Occidentale, F-29200 Brest, France
| | - Mostafa El Habib Daho
- Inserm, UMR 1101 LaTIM, F-29200 Brest, France
- Univ Bretagne Occidentale, F-29200 Brest, France
| | - Pierre-Henri Conze
- Inserm, UMR 1101 LaTIM, F-29200 Brest, France
- IMT Atlantique, ITI Department, F-29200 Brest, France
| | - Rachid Zeghlache
- Inserm, UMR 1101 LaTIM, F-29200 Brest, France
- Univ Bretagne Occidentale, F-29200 Brest, France
| | - Hugo Le Boité
- Sorbonne University, F-75006 Paris, France
- Service d’Ophtalmologie, Hôpital Lariboisière, AP-HP, F-75475 Paris, France
| | - Sophie Bonnin
- Service d’Ophtalmologie, Hôpital Lariboisière, AP-HP, F-75475 Paris, France
| | | | | | - Bruno Lay
- ADCIS, F-14280 Saint-Contest, France
| | | | - Ramin Tadayoni
- Service d’Ophtalmologie, Hôpital Lariboisière, AP-HP, F-75475 Paris, France
| | - Béatrice Cochener
- Inserm, UMR 1101 LaTIM, F-29200 Brest, France
- Univ Bretagne Occidentale, F-29200 Brest, France
- Service d’Ophtalmologie, CHRU Brest, F-29200 Brest, France
| | - Mathieu Lamard
- Inserm, UMR 1101 LaTIM, F-29200 Brest, France
- Univ Bretagne Occidentale, F-29200 Brest, France
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11
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Teoh CS, Wong KH, Xiao D, Wong HC, Zhao P, Chan HW, Yuen YS, Naing T, Yogesan K, Koh VTC. Variability in Grading Diabetic Retinopathy Using Retinal Photography and Its Comparison with an Automated Deep Learning Diabetic Retinopathy Screening Software. Healthcare (Basel) 2023; 11:1697. [PMID: 37372815 DOI: 10.3390/healthcare11121697] [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: 03/19/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) screening using colour retinal photographs is cost-effective and time-efficient. In real-world clinical settings, DR severity is frequently graded by individuals of different expertise levels. We aim to determine the agreement in DR severity grading between human graders of varying expertise and an automated deep learning DR screening software (ADLS). METHODS Using the International Clinical DR Disease Severity Scale, two hundred macula-centred fundus photographs were graded by retinal specialists, ophthalmology residents, family medicine physicians, medical students, and the ADLS. Based on referral urgency, referral grading was divided into no referral, non-urgent referral, and urgent referral to an ophthalmologist. Inter-observer and intra-group variations were analysed using Gwet's agreement coefficient, and the performance of ADLS was evaluated using sensitivity and specificity. RESULTS The agreement coefficient for inter-observer and intra-group variability ranged from fair to very good, and moderate to good, respectively. The ADLS showed a high area under curve of 0.879, 0.714, and 0.836 for non-referable DR, non-urgent referable DR, and urgent referable DR, respectively, with varying sensitivity and specificity values. CONCLUSION Inter-observer and intra-group agreements among human graders vary widely, but ADLS is a reliable and reasonably sensitive tool for mass screening to detect referable DR and urgent referable DR.
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Affiliation(s)
- Chin Sheng Teoh
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Kah Hie Wong
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Di Xiao
- Commonwealth Scientific and Industrial Research Organisation, Urrbrae 5064, Australia
| | - Hung Chew Wong
- Medicine Biostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Paul Zhao
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Hwei Wuen Chan
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Yew Sen Yuen
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | - Thet Naing
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
| | | | - Victor Teck Chang Koh
- Department of Ophthalmology, National University Health System, Singapore 119228, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
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12
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Nadgir R. Why Are We Still Sitting in the Dark? Radiology as a Career Choice in the Setting of an Emerging Technology Revolution. Acad Radiol 2023; 30:1189-1191. [PMID: 37061451 DOI: 10.1016/j.acra.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 03/13/2023] [Indexed: 04/17/2023]
Affiliation(s)
- Rohini Nadgir
- Johns Hopkins Medicine, 600 N. Wolfe Street, Baltimore, MD.
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