1
|
Rajabi MT, Sadeghi R, Abdol Homayuni MR, Pezeshgi S, Hosseini SS, Rajabi MB, Poshtdar S. Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review. BMC Ophthalmol 2024; 24:304. [PMID: 39039451 PMCID: PMC11265183 DOI: 10.1186/s12886-024-03569-5] [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: 04/05/2024] [Accepted: 07/10/2024] [Indexed: 07/24/2024] Open
Abstract
PURPOSE To evaluate the evidence for alterations of blood flow, vascular and perfusion densities in the choroid, macula, peripapillary region, and the area surrounding the optic nerve head (ONH) in patients with thyroid-associated ophthalmopathy (TAO) based on changes of OCTA parameters. METHODS A systematic review of Pubmed, Google Scholar, Scopus, WOS, Cochrane, and Embase databases, including quality assessment of published studies, investigating the alterations of OCTA parameters in TAO patients was conducted. The outcomes of interest comprised changes of perfusion and vascular densities in radial peripapillary capillary (RPC), ONH, superficial and deep retinal layers (SRL and DRL), choriocapillaris (CC) flow, and the extent of the foveal avascular zone (FAZ). RESULTS From the total of 1253 articles obtained from the databases, the pool of papers was narrowed down to studies published until March 20th, 2024. Lastly, 42 studies were taken into consideration which contained the data regarding the alterations of OCTA parameters including choriocapillary vascular flow, vascular and perfusion densities of retinal microvasculature, SRL, and DRL, changes in macular all grid sessions, changes of foveal, perifoveal and parafoveal densities, macular whole image vessel density (m-wiVD) and FAZ, in addition to alterations of ONH and RPC whole image vessel densities (onh-wiVD and rpc-wiVD) among TAO patients. The correlation of these parameters with visual field-associated parameters, such as Best-corrected visual acuity (BCVA), Visual field mean defect (VF-MD), axial length (AL), P100 amplitude, and latency, was also evaluated among TAO patients. CONCLUSION The application of OCTA has proven helpful in distinguishing active and inactive TAO patients, as well as differentiation of patients with or without DON, indicating the potential promising role of some OCTA measures for early detection of TAO with high sensitivity and specificity in addition to preventing the irreversible outcomes of TAO. OCTA assessments have also been applied to evaluate the effectiveness of TAO treatment approaches, including systemic corticosteroid therapy and surgical decompression.
Collapse
Affiliation(s)
- Mohammad Taher Rajabi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran
| | - Reza Sadeghi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Abdol Homayuni
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Saharnaz Pezeshgi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyedeh Simindokht Hosseini
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran
| | - Mohammad Bagher Rajabi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran
| | - Sepideh Poshtdar
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Zip Code: 1336616351, Tehran, Iran.
- Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
2
|
Delsoz M, Madadi Y, Raja H, Munir WM, Tamm B, Mehravaran S, Soleimani M, Djalilian A, Yousefi S. Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. Cornea 2024; 43:664-670. [PMID: 38391243 DOI: 10.1097/ico.0000000000003492] [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: 09/26/2023] [Accepted: 12/28/2023] [Indexed: 02/24/2024]
Abstract
PURPOSE The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. METHODS We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, and degenerations from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT-3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses, compared them with the diagnoses made by 3 corneal specialists (human experts), and evaluated interobserver agreements. RESULTS The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct of 20 cases), whereas the accuracy of ChatGPT-3.5 was 60% (12 correct cases of 20). The accuracy of 3 corneal specialists compared with ChatGPT-4.0 and ChatGPT-3.5 was 100% (20 cases, P = 0.23, P = 0.0033), 90% (18 cases, P = 0.99, P = 0.6), and 90% (18 cases, P = 0.99, P = 0.6), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases), whereas the interobserver agreement between ChatGPT-4.0 and 3 corneal specialists was 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of 3 corneal specialists was 60% (12 cases). CONCLUSIONS The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration. A balanced approach that combines artificial intelligence-generated insights with clinical expertise holds a key role for unveiling its full potential in eye care.
Collapse
Affiliation(s)
- Mohammad Delsoz
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Yeganeh Madadi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Hina Raja
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
| | - Wuqaas M Munir
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD
| | - Brendan Tamm
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD
| | - Shiva Mehravaran
- Department of Biology, School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD
| | - Mohammad Soleimani
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran ; and
| | - Ali Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL
| | - Siamak Yousefi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, TN
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN
| |
Collapse
|
3
|
Biousse V, Najjar RP, Tang Z, Lin MY, Wright DW, Keadey MT, Wong TY, Bruce BB, Milea D, Newman NJ. Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department. Am J Ophthalmol 2024; 261:199-207. [PMID: 37926337 DOI: 10.1016/j.ajo.2023.10.025] [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: 05/31/2023] [Revised: 10/26/2023] [Accepted: 10/26/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic Nerve Study Artificial Intelligence (BONSAI) deep learning system on nonmydriatic fundus photographs from the FOTO-ED studies to determine if the deep learning system could have improved the detection of papilledema had it been available to ED providers as a real-time diagnostic aid. DESIGN Retrospective secondary analysis of a cohort of patients included in the FOTO-ED studies. METHODS The testing data set included 1608 photographs obtained from 828 patients in the FOTO-ED studies. Photographs were reclassified according to the optic disc classification system used by the deep learning system ("normal optic discs," "papilledema," and "other optic disc abnormalities"). The system's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 1-vs-rest strategy, with reference to expert neuro-ophthalmologists. RESULTS The BONSAI deep learning system successfully distinguished normal from abnormal optic discs (AUC 0.92 [95% confidence interval {CI} 0.90-0.93]; sensitivity 75.6% [73.7%-77.5%] and specificity 89.6% [86.3%-92.8%]), and papilledema from normal and others (AUC 0.97 [0.95-0.99]; sensitivity 84.0% [75.0%-92.6%] and specificity 98.9% [98.5%-99.4%]). Six patients with missed papilledema in 1 eye were correctly identified by the deep learning system as having papilledema in the other eye. CONCLUSIONS The BONSAI deep learning system was able to reliably identify papilledema and normal optic discs on nonmydriatic photographs obtained in the FOTO-ED studies. Our deep learning system has excellent potential as a diagnostic aid in EDs and non-ophthalmology clinics equipped with nonmydriatic fundus cameras. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.
Collapse
Affiliation(s)
- Valérie Biousse
- From the Department of Ophthalmology (V.B., M.Y.L., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA; Department of Neurology (V.B., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA.
| | - Raymond P Najjar
- Singapore Eye Research Institute and Singapore National Eye Centre (R.P.N., Z.T., T.Y.W., D.M.), Singapore; Duke-NUS Medical School (R.P.N., T.Y.W., D.M.), National University of Singapore, Singapore; Eye N' Brain Research Group (R.P.N.), Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Center for Innovation and Precision Eye Health (R.P.N.), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zhiqun Tang
- Singapore Eye Research Institute and Singapore National Eye Centre (R.P.N., Z.T., T.Y.W., D.M.), Singapore
| | - Mung Yan Lin
- From the Department of Ophthalmology (V.B., M.Y.L., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA
| | - David W Wright
- Department of Emergency Medicine (D.W.W., M.T.K.), Emory University School of Medicine, Atlanta, Georgia, USA
| | - Matthew T Keadey
- Department of Emergency Medicine (D.W.W., M.T.K.), Emory University School of Medicine, Atlanta, Georgia, USA
| | - Tien Y Wong
- Singapore Eye Research Institute and Singapore National Eye Centre (R.P.N., Z.T., T.Y.W., D.M.), Singapore; Duke-NUS Medical School (R.P.N., T.Y.W., D.M.), National University of Singapore, Singapore; Tsinghua Medicine (T.Y.W.), Tsinghua University, China
| | - Beau B Bruce
- From the Department of Ophthalmology (V.B., M.Y.L., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA; Department of Neurology (V.B., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA; Rollins School of Public Health (B.B.B.), Emory University School of Medicine, Atlanta, Georgia, USA
| | - Dan Milea
- Singapore Eye Research Institute and Singapore National Eye Centre (R.P.N., Z.T., T.Y.W., D.M.), Singapore; Duke-NUS Medical School (R.P.N., T.Y.W., D.M.), National University of Singapore, Singapore
| | - Nancy J Newman
- From the Department of Ophthalmology (V.B., M.Y.L., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA; Department of Neurology (V.B., B.B.B., N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA; Department of Neurological Surgery (N.J.N.), Emory University School of Medicine, Atlanta, Georgia, USA
| |
Collapse
|
4
|
Wu TE, Chen JW, Liu TC, Yu CH, Jhou MJ, Lu CJ. Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines. J Pers Med 2024; 14:125. [PMID: 38276247 PMCID: PMC10817583 DOI: 10.3390/jpm14010125] [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: 12/27/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
PURPOSE The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication. METHODS Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP. RESULTS The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg. CONCLUSIONS MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.
Collapse
Affiliation(s)
- Tzu-En Wu
- Department of Ophthalmology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
- School of Medicine, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| | - Jun-Wei Chen
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| | - Chieh-Han Yu
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Mao-Jhen Jhou
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 24205, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 24205, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| |
Collapse
|
5
|
Delsoz M, Madadi Y, Munir WM, Tamm B, Mehravaran S, Soleimani M, Djalilian A, Yousefi S. Performance of ChatGPT in Diagnosis of Corneal Eye Diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.25.23294635. [PMID: 37720035 PMCID: PMC10500623 DOI: 10.1101/2023.08.25.23294635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Introduction Assessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts. Methods We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, degenerations, and injuries from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses then compared with the diagnoses of three cornea specialists (Human experts) and evaluated interobserver agreements. Results The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of three cornea specialists was 60% (12 cases). Conclusions The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration.
Collapse
Affiliation(s)
- Mohammad Delsoz
- Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yeganeh Madadi
- Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Wuqaas M Munir
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brendan Tamm
- Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shiva Mehravaran
- School of Computer, Mathematical, and Natural Sciences, Morgan State University, Baltimore, MD, USA
| | - Mohammad Soleimani
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Djalilian
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Siamak Yousefi
- Hamilton Eye Institute, Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| |
Collapse
|
6
|
García-Domínguez A, Galván-Tejada CE, Magallanes-Quintanar R, Gamboa-Rosales H, Curiel IG, Peralta-Romero J, Cruz M. Diabetes Detection Models in Mexican Patients by Combining Machine Learning Algorithms and Feature Selection Techniques for Clinical and Paraclinical Attributes: A Comparative Evaluation. J Diabetes Res 2023; 2023:9713905. [PMID: 37404324 PMCID: PMC10317588 DOI: 10.1155/2023/9713905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/08/2023] [Accepted: 06/18/2023] [Indexed: 07/06/2023] Open
Abstract
The development of medical diagnostic models to support healthcare professionals has witnessed remarkable growth in recent years. Among the prevalent health conditions affecting the global population, diabetes stands out as a significant concern. In the domain of diabetes diagnosis, machine learning algorithms have been widely explored for generating disease detection models, leveraging diverse datasets primarily derived from clinical studies. The performance of these models heavily relies on the selection of the classifier algorithm and the quality of the dataset. Therefore, optimizing the input data by selecting relevant features becomes essential for accurate classification. This research presents a comprehensive investigation into diabetes detection models by integrating two feature selection techniques: the Akaike information criterion and genetic algorithms. These techniques are combined with six prominent classifier algorithms, including support vector machine, random forest, k-nearest neighbor, gradient boosting, extra trees, and naive Bayes. By leveraging clinical and paraclinical features, the generated models are evaluated and compared to existing approaches. The results demonstrate superior performance, surpassing accuracies of 94%. Furthermore, the use of feature selection techniques allows for working with a reduced dataset. The significance of feature selection is underscored in this study, showcasing its pivotal role in enhancing the performance of diabetes detection models. By judiciously selecting relevant features, this approach contributes to the advancement of medical diagnostic capabilities and empowers healthcare professionals in making informed decisions regarding diabetes diagnosis and treatment.
Collapse
Affiliation(s)
- Antonio García-Domínguez
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Carlos E. Galván-Tejada
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Rafael Magallanes-Quintanar
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Hamurabi Gamboa-Rosales
- Academic Unit of Electrical Engineering, Autonomous University of Zacatecas, Juárez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Irma González Curiel
- Academic Unit of Chemical Sciences, Autonomous University of Zacatecas, Juarez Garden 147, Downtown, Zacatecas 98000, Mexico
| | - Jesús Peralta-Romero
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center Siglo XXI, Mexican Social Security Institute, Mexico City, Mexico
| | - Miguel Cruz
- Medical Research Unit in Biochemistry, Specialties Hospital, National Medical Center Siglo XXI, Mexican Social Security Institute, Mexico City, Mexico
| |
Collapse
|
7
|
Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Front Cell Dev Biol 2023; 11:1168327. [PMID: 37056999 PMCID: PMC10086262 DOI: 10.3389/fcell.2023.1168327] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.
Collapse
Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
| | - Yunfang Liu
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
| | - Ying Zhao
- Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
| | - Liya Zhang
- Department of Ophthalmology, The First People’s Hospital of Huzhou, Huzhou, Zhejiang, China
- *Correspondence: Liya Zhang, ; Ying Zhao,
| |
Collapse
|
8
|
Wang S, Ji Y, Bai W, Ji Y, Li J, Yao Y, Zhang Z, Jiang Q, Li K. Advances in artificial intelligence models and algorithms in the field of optometry. Front Cell Dev Biol 2023; 11:1170068. [PMID: 37187617 PMCID: PMC10175695 DOI: 10.3389/fcell.2023.1170068] [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: 02/27/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.
Collapse
Affiliation(s)
- Suyu Wang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yuke Ji
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wen Bai
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jiajun Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yujia Yao
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ziran Zhang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Qin Jiang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
| | - Keran Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
| |
Collapse
|