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Hosseini F, Asadi F, Rabiei R, Kiani F, Harari RE. Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review. Surv Ophthalmol 2024:S0039-6257(24)00073-0. [PMID: 38942125 DOI: 10.1016/j.survophthal.2024.06.005] [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: 03/07/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024]
Abstract
Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in "detection", "classification" and "segmentation" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96% in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.
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Affiliation(s)
- Farhang Hosseini
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Farkhondeh Asadi
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Rabiei
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Fatemeh Kiani
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Rayan Ebnali Harari
- STRATUS Center for Medical Simulation, Harvard Medical School, Boston, MA, USA.
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Akpinar MH, Sengur A, Faust O, Tong L, Molinari F, Acharya UR. Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108253. [PMID: 38861878 DOI: 10.1016/j.cmpb.2024.108253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/22/2024] [Accepted: 05/25/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND AND OBJECTIVES Optical coherence tomography (OCT) has ushered in a transformative era in the domain of ophthalmology, offering non-invasive imaging with high resolution for ocular disease detection. OCT, which is frequently used in diagnosing fundamental ocular pathologies, such as glaucoma and age-related macular degeneration (AMD), plays an important role in the widespread adoption of this technology. Apart from glaucoma and AMD, we will also investigate pertinent pathologies, such as epiretinal membrane (ERM), macular hole (MH), macular dystrophy (MD), vitreomacular traction (VMT), diabetic maculopathy (DMP), cystoid macular edema (CME), central serous chorioretinopathy (CSC), diabetic macular edema (DME), diabetic retinopathy (DR), drusen, glaucomatous optic neuropathy (GON), neovascular AMD (nAMD), myopia macular degeneration (MMD) and choroidal neovascularization (CNV) diseases. This comprehensive review examines the role that OCT-derived images play in detecting, characterizing, and monitoring eye diseases. METHOD The 2020 PRISMA guideline was used to structure a systematic review of research on various eye conditions using machine learning (ML) or deep learning (DL) techniques. A thorough search across IEEE, PubMed, Web of Science, and Scopus databases yielded 1787 publications, of which 1136 remained after removing duplicates. Subsequent exclusion of conference papers, review papers, and non-open-access articles reduced the selection to 511 articles. Further scrutiny led to the exclusion of 435 more articles due to lower-quality indexing or irrelevance, resulting in 76 journal articles for the review. RESULTS During our investigation, we found that a major challenge for ML-based decision support is the abundance of features and the determination of their significance. In contrast, DL-based decision support is characterized by a plug-and-play nature rather than relying on a trial-and-error approach. Furthermore, we observed that pre-trained networks are practical and especially useful when working on complex images such as OCT. Consequently, pre-trained deep networks were frequently utilized for classification tasks. Currently, medical decision support aims to reduce the workload of ophthalmologists and retina specialists during routine tasks. In the future, it might be possible to create continuous learning systems that can predict ocular pathologies by identifying subtle changes in OCT images.
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Affiliation(s)
- Muhammed Halil Akpinar
- Department of Electronics and Automation, Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Abdulkadir Sengur
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Louis Tong
- Singapore Eye Research Institute, Singapore, Singapore
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Zhou T, Gu S, Shao F, Li P, Wu Y, Xiong J, Wang B, Zhou C, Gao P, Hua X. Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study. J Hypertens 2024; 42:701-710. [PMID: 38230614 PMCID: PMC10906188 DOI: 10.1097/hjh.0000000000003658] [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: 08/22/2023] [Revised: 12/01/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]
Abstract
INTRODUCTION Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnancies. METHODS This prospective cohort study was conducted at Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Eligible participants included singleton pregnancies who presented for prenatal visits before 14 weeks of gestation from September 1, 2020, to February 1, 2022. Retinal fundus images were obtained using a nonmydriatic digital retinal camera during their initial prenatal visit upon admission before 20 weeks of gestation. In addition, we generated fundus scores, which indicated the predictive value of hypertension, using a hypertension detection model. To evaluate the predictive value of the retinal fundus image-based deep learning algorithm for preeclampsia, we conducted stratified analyses and measured the area under the curve (AUC), sensitivity, and specificity. We then conducted sensitivity analyses for validation. RESULTS Our study analyzed a total of 1138 women, 92 pregnancies developed into hypertension disorders of pregnancy (HDP), including 26 cases of gestational hypertension and 66 cases of preeclampsia. The adjusted odds ratio (aOR) of the fundus scores was 2.582 (95% CI, 1.883-3.616; P < 0.001). Otherwise, in the categories of prepregnancy BMI less than 28.0 and at least 28.0, the aORs were 3.073 (95%CI, 2.265-4.244; P < 0.001) and 5.866 (95% CI, 3.292-11.531; P < 0.001). In the categories of maternal age less than 35.0 and at least 35.0, the aORs were 2.845 (95% CI, 1.854-4.463; P < 0.001) and 2.884 (95% CI, 1.794-4.942; P < 0.001). The AUC of the fundus score combined with risk factors was 0.883 (sensitivity, 0.722; specificity, 0.934; 95% CI, 0.834-0.932) for predicting preeclampsia. CONCLUSION Our study demonstrates that the use of deep learning algorithm-based retinal fundus images offers promising predictive value for the early detection of preeclampsia.
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Affiliation(s)
- Tianfan Zhou
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Shengyi Gu
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Feixue Shao
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Ping Li
- Department of Ophthalmology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University
| | - Yuelin Wu
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | | | - Bin Wang
- Beijing Airdoc Technology Co., Ltd., Beijing, China
| | - Chenchen Zhou
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
| | - Peng Gao
- Department of Ophthalmology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University
| | - Xiaolin Hua
- Department of Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University
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Mihalache A, Huang RS, Popovic MM, Patil NS, Pandya BU, Shor R, Pereira A, Kwok JM, Yan P, Wong DT, Kertes PJ, Muni RH. Accuracy of an Artificial Intelligence Chatbot's Interpretation of Clinical Ophthalmic Images. JAMA Ophthalmol 2024; 142:321-326. [PMID: 38421670 PMCID: PMC10905373 DOI: 10.1001/jamaophthalmol.2024.0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 12/19/2023] [Indexed: 03/02/2024]
Abstract
Importance Ophthalmology is reliant on effective interpretation of multimodal imaging to ensure diagnostic accuracy. The new ability of ChatGPT-4 (OpenAI) to interpret ophthalmic images has not yet been explored. Objective To evaluate the performance of the novel release of an artificial intelligence chatbot that is capable of processing imaging data. Design, Setting, and Participants This cross-sectional study used a publicly available dataset of ophthalmic cases from OCTCases, a medical education platform based out of the Department of Ophthalmology and Vision Sciences at the University of Toronto, with accompanying clinical multimodal imaging and multiple-choice questions. Across 137 available cases, 136 contained multiple-choice questions (99%). Exposures The chatbot answered questions requiring multimodal input from October 16 to October 23, 2023. Main Outcomes and Measures The primary outcome was the accuracy of the chatbot in answering multiple-choice questions pertaining to image recognition in ophthalmic cases, measured as the proportion of correct responses. χ2 Tests were conducted to compare the proportion of correct responses across different ophthalmic subspecialties. Results A total of 429 multiple-choice questions from 136 ophthalmic cases and 448 images were included in the analysis. The chatbot answered 299 of multiple-choice questions correctly across all cases (70%). The chatbot's performance was better on retina questions than neuro-ophthalmology questions (77% vs 58%; difference = 18%; 95% CI, 7.5%-29.4%; χ21 = 11.4; P < .001). The chatbot achieved a better performance on nonimage-based questions compared with image-based questions (82% vs 65%; difference = 17%; 95% CI, 7.8%-25.1%; χ21 = 12.2; P < .001).The chatbot performed best on questions in the retina category (77% correct) and poorest in the neuro-ophthalmology category (58% correct). The chatbot demonstrated intermediate performance on questions from the ocular oncology (72% correct), pediatric ophthalmology (68% correct), uveitis (67% correct), and glaucoma (61% correct) categories. Conclusions and Relevance In this study, the recent version of the chatbot accurately responded to approximately two-thirds of multiple-choice questions pertaining to ophthalmic cases based on imaging interpretation. The multimodal chatbot performed better on questions that did not rely on the interpretation of imaging modalities. As the use of multimodal chatbots becomes increasingly widespread, it is imperative to stress their appropriate integration within medical contexts.
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Affiliation(s)
- Andrew Mihalache
- Temerty School of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ryan S. Huang
- Temerty School of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Marko M. Popovic
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Nikhil S. Patil
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Bhadra U. Pandya
- Temerty School of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Reut Shor
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Austin Pereira
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Jason M. Kwok
- Temerty School of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Peng Yan
- Temerty School of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
| | - David T. Wong
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology, St Michael’s Hospital/Unity Health Toronto, Toronto, Ontario, Canada
| | - Peter J. Kertes
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
- John and Liz Tory Eye Centre, Sunnybrook Health Science Centre, Toronto, Ontario, Canada
| | - Rajeev H. Muni
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology, St Michael’s Hospital/Unity Health Toronto, Toronto, Ontario, Canada
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Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering (Basel) 2023; 10:1435. [PMID: 38136026 PMCID: PMC10740686 DOI: 10.3390/bioengineering10121435] [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: 11/18/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.
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Affiliation(s)
- Luís Pinto-Coelho
- ISEP—School of Engineering, Polytechnic Institute of Porto, 4200-465 Porto, Portugal;
- INESCTEC, Campus of the Engineering Faculty of the University of Porto, 4200-465 Porto, Portugal
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Yamane H, Itagaki T, Kajitani K, Koura Y, Kawabuchi Y, Ohara M. Cystoid Macular Edema following Treatment with Nanoparticle Albumin-Bound Paclitaxel and Atezolizumab for Metastatic Breast Cancer. Case Rep Oncol 2023; 16:1121-1128. [PMID: 37900858 PMCID: PMC10601834 DOI: 10.1159/000533999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/24/2023] [Indexed: 10/31/2023] Open
Abstract
Cystoid macular edema (CME) is a rare side effect associated with chemotherapy. Although the development of CME has been reported to occur following treatment with taxane drugs, such as nanoparticle albumin-bound paclitaxel (Nab-PTX), the occurrence of CME with treatment with atezolizumab has not yet been reported. Here, we report the case of a 49-year-old woman who developed CME 19 months into chemotherapy with Nab-PTX and atezolizumab. Improvement was not achieved with steroid injections into the Tenon's sac, and Nab-PTX and atezolizumab treatments were ceased. One month later, there was subjective improvement in her symptoms. Although many reports have indicated that cessation of chemotherapy has successfully improved CME, a specific treatment for CME has not yet been established. Clinicians should be aware of the ophthalmologic side effects and offer immediate treatment if symptoms develop.
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Affiliation(s)
- Hiroaki Yamane
- Department of Breast Surgery, Hiroshima General Hospital, Hiroshima, Japan
- Department of Surgery, Yamane Clinic, Hiroshima, Japan
| | - Tomoko Itagaki
- Department of Breast Surgery, Hiroshima General Hospital, Hiroshima, Japan
| | - Keiko Kajitani
- Department of Breast Surgery, Hiroshima General Hospital, Hiroshima, Japan
| | - Yuji Koura
- Department of Ophthalmology, Koura Eye Clinic, Hiroshima, Japan
| | - Yoshiharu Kawabuchi
- Department of Breast Surgery, Hatsukaichi Breast Care Clinic, Hiroshima, Japan
| | - Masahiro Ohara
- Department of Breast Surgery, Hiroshima General Hospital, Hiroshima, Japan
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