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Lange M, Löwe A, Kayser I, Schaller A. Approaches for the Use of AI in Workplace Health Promotion and Prevention: Systematic Scoping Review. JMIR AI 2024; 3:e53506. [PMID: 38989904 PMCID: PMC11372327 DOI: 10.2196/53506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/02/2024] [Accepted: 07/10/2024] [Indexed: 07/12/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people's health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or biomedicine, the transition of AI into WHPP research has yet to be systematically investigated. OBJECTIVE The systematic scoping review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes? METHODS A systematic scoping literature review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized. RESULTS A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework. CONCLUSIONS Although AI has caught increasing attention in health-related research, the review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines. TRIAL REGISTRATION OSF Registries osf.io/bfswp; https://osf.io/bfswp.
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Affiliation(s)
- Martin Lange
- Department of Fitness & Health, IST University of Applied Sciences, Duesseldorf, Germany
| | - Alexandra Löwe
- Department of Fitness & Health, IST University of Applied Sciences, Duesseldorf, Germany
| | - Ina Kayser
- Department of Communication & Business, IST University of Applied Sciences, Duesseldorf, Germany
| | - Andrea Schaller
- Institute of Sport Science, Department of Human Sciences, University of the Bundeswehr Munich, Munich, Germany
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2
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Verma PK, Kaur J. Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1783-1799. [PMID: 38438695 PMCID: PMC11300804 DOI: 10.1007/s10278-024-01010-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 03/06/2024]
Abstract
Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models; look at the most popular datasets; and evaluate results in this Literature.
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Affiliation(s)
- Prem Kumari Verma
- Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India.
| | - Jagdeep Kaur
- Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, 144008, Punjab, India
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3
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von der Emde L, Künzel SH, Pfau M, Morelle O, Liermann Y, Chang P, Pfau K, Thiele S, Holz FG. [Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration]. DIE OPHTHALMOLOGIE 2024; 121:609-615. [PMID: 39083095 DOI: 10.1007/s00347-024-02078-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/20/2024] [Accepted: 06/20/2024] [Indexed: 08/03/2024]
Abstract
Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manifestations, which range from individual small drusen, hyperpigmentation, hypopigmentation up to early stages of geographical atrophy. Current high-resolution imaging technologies enable an accurate detection and description of anatomical features, such as drusen volumes, hyperreflexive foci and photoreceptor degeneration, which are risk factors that are decisive for prediction of the course of the disease; however, the manual annotation of these features in complex optical coherence tomography (OCT) scans is impractical for the routine clinical practice and research. In this context AI provides a solution by fully automatic segmentation and therefore delivers exact, reproducible and quantitative analyses of AMD-related biomarkers. Furthermore, the application of AI in iAMD facilitates the risk assessment and the development of structural endpoints for new forms of treatment. For example, the quantitative analysis of drusen volume and hyperreflective foci with AI algorithms has shown a correlation with the progression of the disease. These technological advances therefore improve not only the diagnostic precision but also support future targeted treatment strategies and contribute to the prioritized target of personalized medicine in the diagnostics and treatment of AMD.
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Affiliation(s)
- Leon von der Emde
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
| | - Sandrine H Künzel
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Maximilian Pfau
- Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz
| | - Olivier Morelle
- Institut für Informatik 2, visual computing, Universität Bonn, Bonn, Deutschland
| | - Yannick Liermann
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Petrus Chang
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
| | - Kristina Pfau
- Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz
| | - Sarah Thiele
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
- Klinik für Augenheilkunde, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Frank G Holz
- Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland
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Abd El-Khalek AA, Balaha HM, Sewelam A, Ghazal M, Khalil AT, Abo-Elsoud MEA, El-Baz A. A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD). Bioengineering (Basel) 2024; 11:711. [PMID: 39061793 PMCID: PMC11273790 DOI: 10.3390/bioengineering11070711] [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: 06/12/2024] [Revised: 07/02/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain.
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Affiliation(s)
- Aya A. Abd El-Khalek
- Communications and Electronics Engineering Department, Nile Higher Institute for Engineering and Technology, Mansoura 35511, Egypt;
| | - Hossam Magdy Balaha
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
| | - Ashraf Sewelam
- Ophthalmology Department, Faculty of Medicine, Mansoura University, Mansoura 35511, Egypt;
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Abeer T. Khalil
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (A.T.K.); (M.E.A.A.-E.)
| | - Mohy Eldin A. Abo-Elsoud
- Communications and Electronics Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35511, Egypt; (A.T.K.); (M.E.A.A.-E.)
| | - Ayman El-Baz
- Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
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Bhumika, Bora NS, Bora PS. Genetic Insights into Age-Related Macular Degeneration. Biomedicines 2024; 12:1479. [PMID: 39062052 PMCID: PMC11274963 DOI: 10.3390/biomedicines12071479] [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: 04/24/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/28/2024] Open
Abstract
One of the major causes of vision impairment among elderly people in developed nations is age-related macular degeneration (AMD). The distinctive features of AMD are the accumulation of extracellular deposits called drusen and the gradual deterioration of photoreceptors and nearby tissues in the macula. AMD is a complex and multifaceted disease influenced by several factors such as aging, environmental risk factors, and a person's genetic susceptibility to the condition. The interaction among these factors leads to the initiation and advancement of AMD, where genetic predisposition plays a crucial role. With the advent of high-throughput genotyping technologies, many novel genetic loci associated with AMD have been identified, enhancing our knowledge of its genetic architecture. The common genetic variants linked to AMD are found on chromosome 1q32 (in the complement factor H gene) and 10q26 (age-related maculopathy susceptibility 2 and high-temperature requirement A serine peptidase 1 genes) loci, along with several other risk variants. This review summarizes the common genetic variants of complement pathways, lipid metabolism, and extracellular matrix proteins associated with AMD risk, highlighting the intricate pathways contributing to AMD pathogenesis. Knowledge of the genetic underpinnings of AMD will allow for the future development of personalized diagnostics and targeted therapeutic interventions, paving the way for more effective management of AMD and improved outcomes for affected individuals.
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Affiliation(s)
- Bhumika
- Department of Zoology, Sunderwati Mahila College, Tilka Manjhi Bhagalpur University, Bihar 812007, India;
| | - Nalini S. Bora
- Pat & Willard Walker Eye Research Center, Department of Ophthalmology, Jones Eye Institute, University of Arkansas for Medical Sciences, 4301 West Markham, Little Rock, AR 72205, USA;
| | - Puran S. Bora
- Pat & Willard Walker Eye Research Center, Department of Ophthalmology, Jones Eye Institute, University of Arkansas for Medical Sciences, 4301 West Markham, Little Rock, AR 72205, USA;
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Joseph S, Selvaraj J, Mani I, Kumaragurupari T, Shang X, Mudgil P, Ravilla T, He M. Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis. Am J Ophthalmol 2024; 263:214-230. [PMID: 38438095 DOI: 10.1016/j.ajo.2024.02.012] [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: 07/22/2023] [Revised: 02/03/2024] [Accepted: 02/12/2024] [Indexed: 03/06/2024]
Abstract
PURPOSE To evaluate the diagnostic accuracy of artificial intelligence (AI)-based automated diabetic retinopathy (DR) screening in real-world settings. DESIGN Systematic review and meta-analysis METHODS: We conducted a systematic review of relevant literature from January 2012 to August 2022 using databases including PubMed, Scopus and Web of Science. The quality of studies was evaluated using Quality Assessment for Diagnostic Accuracy Studies 2 (QUADAS-2) checklist. We calculated pooled accuracy, sensitivity, specificity, and diagnostic odds ratio (DOR) as summary measures. The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO - CRD42022367034). RESULTS We included 34 studies which utilized AI algorithms for diagnosing DR based on real-world fundus images. Quality assessment of these studies indicated a low risk of bias and low applicability concern. Among gradable images, the overall pooled accuracy, sensitivity, specificity, and DOR were 81%, 94% (95% CI: 92.0-96.0), 89% (95% CI: 85.0-92.0) and 128 (95% CI: 80-204) respectively. Sub-group analysis showed that, when acceptable quality imaging could be obtained, non-mydriatic fundus images had a better DOR of 143 (95% CI: 82-251) and studies using 2 field images had a better DOR of 161 (95% CI 74-347). Our meta-regression analysis revealed a statistically significant association between DOR and variables such as the income status, and the type of fundus camera. CONCLUSION Our findings indicate that AI algorithms have acceptable performance in screening for DR using fundus images compared to human graders. Implementing a fundus camera with AI-based software has the potential to assist ophthalmologists in reducing their workload and improving the accuracy of DR diagnosis.
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Affiliation(s)
- Sanil Joseph
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia; Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India.
| | - Jerrome Selvaraj
- Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India
| | - Iswarya Mani
- Aravind Eye Hospital and Postgraduate Institute of Ophthalmology (I.M, T.K), Madurai, India
| | | | - Xianwen Shang
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia
| | - Poonam Mudgil
- School of Medicine (P.M), Western Sydney University, Campbell town, Australia; School of Rural Medicine (P.M), Charles Sturt University, Orange, NSW, Australia
| | - Thulasiraj Ravilla
- Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India
| | - Mingguang He
- From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia
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Cnaany Y, Lender R, Chowers I, Tiosano L, Shwartz Y, Levy J. An automated process for bulk downloading optical coherence tomography scans. Graefes Arch Clin Exp Ophthalmol 2024; 262:2145-2151. [PMID: 38416238 PMCID: PMC11222237 DOI: 10.1007/s00417-024-06420-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 02/06/2024] [Accepted: 02/14/2024] [Indexed: 02/29/2024] Open
Abstract
OBJECTIVE To develop an automated method for efficiently downloading a large number of optical coherence tomography (OCT) scans obtained using the Heidelberg Spectralis (Heidelberg Engineering, Heidelberg, Germany) platform. METHODS The electronic medical records and OCT scans were extracted for all patients with age-related macular degeneration treated at the Hadassah University Hospital Retina Clinic between 2010 and 2021. A macro was created using Visual Basic for Applications (VBA) and Microsoft Excel to automate the export process and anonymize the OCT scans in accordance with hospital policy. OCT scans were extracted as proprietary Heidelberg E2E files. RESULTS The VBA macro was used to export a total of 94,789 E2E files from 2807 patient records, with an average processing time of 4.32 min per volume scan (SD: 3.57 min). The entire export process took a total of approximately 202 h to complete over a period of 24 days. In a smaller sample, using the macro to download the scans was significantly faster than manually downloading the scans, averaging 3.88 vs. 11.08 min/file, respectively (t = 8.59, p < 0.001). Finally, we found that exporting the files during both off-clinic and working hours resulted in significantly faster processing times compared to exporting the files solely during working hours (t = 5.77, p < 0.001). CONCLUSIONS This study demonstrates the feasibility of using VBA and Excel to automate the process for bulk downloading data from a specific medical imaging platform. The specific steps and techniques will likely vary depending on the software used and hospital constraints and should be determined for each application.
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Affiliation(s)
- Yaacov Cnaany
- Department of Ophthalmology, Faculty of Medicine, Hadassah University Medical Center, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Rivkah Lender
- Department of Ophthalmology, Faculty of Medicine, Hadassah University Medical Center, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Itay Chowers
- Department of Ophthalmology, Faculty of Medicine, Hadassah University Medical Center, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Liran Tiosano
- Department of Ophthalmology, Faculty of Medicine, Hadassah University Medical Center, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Yahel Shwartz
- Department of Ophthalmology, Faculty of Medicine, Hadassah University Medical Center, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel
| | - Jaime Levy
- Department of Ophthalmology, Faculty of Medicine, Hadassah University Medical Center, The Hebrew University of Jerusalem, 91120, Jerusalem, Israel.
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Shemer A, Cohen M, Altarescu A, Atar-Vardi M, Hecht I, Dubinsky-Pertzov B, Shoshany N, Zmujack S, Or L, Einan-Lifshitz A, Pras E. Diagnostic capabilities of ChatGPT in ophthalmology. Graefes Arch Clin Exp Ophthalmol 2024; 262:2345-2352. [PMID: 38183467 DOI: 10.1007/s00417-023-06363-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 12/04/2023] [Accepted: 12/23/2023] [Indexed: 01/08/2024] Open
Abstract
PURPOSE The purpose of this study is to assess the diagnostic accuracy of ChatGPT in the field of ophthalmology. METHODS This is a retrospective cohort study conducted in one academic tertiary medical center. We reviewed data of patients admitted to the ophthalmology department from 06/2022 to 01/2023. We then created two clinical cases for each patient. The first case is according to the medical history alone (Hx). The second case includes an addition of the clinical examination (Hx and Ex). For each case, we asked for the three most likely diagnoses from ChatGPT, residents, and attendings. Then, we compared the accuracy rates (at least one correct diagnosis) of all groups. Additionally, we evaluated the total duration for completing the assignment between the groups. RESULTS ChatGPT, residents, and attendings evaluated 126 cases from 63 patients (history only or history and exam findings for each patient). ChatGPT achieved a significantly lower accurate diagnosis rate (54%) in the Hx, as compared to the residents (75%; p < 0.01) and attendings (71%; p < 0.01). After adding the clinical examination findings, the diagnosis rate of ChatGPT was 68%, whereas for the residents and the attendings, it increased to 94% (p < 0.01) and 86% (p < 0.01), respectively. ChatGPT was 4 to 5 times faster than the attendings and residents. CONCLUSIONS AND RELEVANCE ChatGPT showed low diagnostic rates in ophthalmology cases compared to residents and attendings based on patient history alone or with additional clinical examination findings. However, ChatGPT completed the task faster than the physicians.
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Affiliation(s)
- Asaf Shemer
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Michal Cohen
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Health Science, Ben-Gurion University of the Negev, South District, Beer-Sheva, Israel
| | - Aya Altarescu
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Maya Atar-Vardi
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Idan Hecht
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Biana Dubinsky-Pertzov
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Nadav Shoshany
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sigal Zmujack
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Lior Or
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Adi Einan-Lifshitz
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Eran Pras
- Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- The Matlow's Ophthalmo-Genetics Laboratory, Department of Ophthalmology, Shamir Medical Center (Formerly Assaf-Harofeh), Tzrifin, Israel
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Aresta G, Araujo T, Reiter GS, Mai J, Riedl S, Grechenig C, Guymer RH, Wu Z, Schmidt-Erfurth U, Bogunovic H. Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD. Transl Vis Sci Technol 2024; 13:7. [PMID: 38874975 PMCID: PMC11182370 DOI: 10.1167/tvst.13.6.7] [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: 10/04/2023] [Accepted: 04/04/2024] [Indexed: 06/15/2024] Open
Abstract
Purpose The subsidence of the outer plexiform layer (OPL) is an important imaging biomarker on optical coherence tomography (OCT) associated with early outer retinal atrophy and a risk factor for progression to geographic atrophy in patients with intermediate age-related macular degeneration (AMD). Deep neural networks (DNNs) for OCT can support automated detection and localization of this biomarker. Methods The method predicts potential OPL subsidence locations on retinal OCTs. A detection module (DM) infers bounding boxes around subsidences with a likelihood score, and a classification module (CM) assesses subsidence presence at the B-scan level. Overlapping boxes between B-scans are combined and scored by the product of the DM and CM predictions. The volume-wise score is the maximum prediction across all B-scans. One development and one independent external data set were used with 140 and 26 patients with AMD, respectively. Results The system detected more than 85% of OPL subsidences with less than one false-positive (FP)/scan. The average area under the curve was 0.94 ± 0.03 for volume-level detection. Similar or better performance was achieved on the independent external data set. Conclusions DNN systems can efficiently perform automated retinal layer subsidence detection in retinal OCT images. In particular, the proposed DNN system detects OPL subsidence with high sensitivity and a very limited number of FP detections. Translational Relevance DNNs enable objective identification of early signs associated with high risk of progression to the atrophic late stage of AMD, ideally suited for screening and assessing the efficacy of the interventions aiming to slow disease progression.
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Affiliation(s)
- Guilherme Aresta
- Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria
| | - Teresa Araujo
- Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria
| | - Gregor S. Reiter
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Julia Mai
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Sophie Riedl
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Christoph Grechenig
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Robyn H. Guymer
- Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia
| | - Zhichao Wu
- Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia
| | - Ursula Schmidt-Erfurth
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Hrvoje Bogunovic
- Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria
- Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
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Prashar J, Tay N. Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis. Eye (Lond) 2024; 38:303-314. [PMID: 37550366 PMCID: PMC10810874 DOI: 10.1038/s41433-023-02680-z] [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: 12/21/2022] [Revised: 07/12/2023] [Accepted: 07/19/2023] [Indexed: 08/09/2023] Open
Abstract
BACKGROUND Pathological myopia (PM) is a major cause of worldwide blindness and represents a serious threat to eye health globally. Artificial intelligence (AI)-based methods are gaining traction in ophthalmology as highly sensitive and specific tools for screening and diagnosis of many eye diseases. However, there is currently a lack of high-quality evidence for their use in the diagnosis of PM. METHODS A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PM was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance. Five electronic databases were searched, results were assessed against the inclusion criteria and a quality assessment was conducted for included studies. Model sensitivity and specificity were pooled using the DerSimonian and Laird (random-effects) model. Subgroup analysis and meta-regression were performed. RESULTS Of 1021 citations identified, 17 studies were included in the systematic review and 11 studies, evaluating 165,787 eyes, were included in the meta-analysis. The area under the summary receiver operator curve (SROC) was 0.9905. The pooled sensitivity was 95.9% [95.5%-96.2%], and the overall pooled specificity was 96.5% [96.3%-96.6%]. The pooled diagnostic odds ratio (DOR) for detection of PM was 841.26 [418.37-1691.61]. CONCLUSIONS This systematic review and meta-analysis provides robust early evidence that AI-based, particularly deep-learning based, diagnostic tools are a highly specific and sensitive modality for the detection of PM. There is potential for such tools to be incorporated into ophthalmic public health screening programmes, particularly in resource-poor areas with a substantial prevalence of high myopia.
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Affiliation(s)
- Jai Prashar
- University College London, London, UK.
- Moorfields Eye Hospital NHS Foundation Trust, London, UK.
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11
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Skevas C, de Olaguer NP, Lleó A, Thiwa D, Schroeter U, Lopes IV, Mautone L, Linke SJ, Spitzer MS, Yap D, Xiao D. Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment. BMC Ophthalmol 2024; 24:51. [PMID: 38302908 PMCID: PMC10832120 DOI: 10.1186/s12886-024-03306-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. METHODS This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. RESULTS A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. CONCLUSIONS The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. TRIAL REGISTRATION Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.
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Affiliation(s)
- Christos Skevas
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | | | - Albert Lleó
- TeleMedC GmbH, Raboisen 32, 20095, Hamburg, Germany
| | - David Thiwa
- Department of Otorhinolaryngology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Ulrike Schroeter
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Inês Valente Lopes
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany.
| | - Luca Mautone
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Stephan J Linke
- Zentrum Sehestaerke, Martinistraße 64, 20251, Hamburg, Germany
| | - Martin Stephan Spitzer
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Daniel Yap
- TeleMedC Pty Ltd, 61 Ubi Avenue 1, #06-11 UBPoint, Singapore, 40894, Singapore
| | - Di Xiao
- TeleMedC Pty Ltd, Brisbane Technology Park, Level 2, 1 Westlink Court, Darra, QLD 4076, Australia
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12
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Samanta A, Alsoudi AF, Rahimy E, Chhablani J, Weng CY. Imaging Modalities for Dry Macular Degeneration. Int Ophthalmol Clin 2024; 64:35-55. [PMID: 38146880 DOI: 10.1097/iio.0000000000000512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
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13
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Heger KA, Waldstein SM. Artificial intelligence in retinal imaging: current status and future prospects. Expert Rev Med Devices 2024; 21:73-89. [PMID: 38088362 DOI: 10.1080/17434440.2023.2294364] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/09/2023] [Indexed: 12/19/2023]
Abstract
INTRODUCTION The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution. AREAS COVERED This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature. EXPERT OPINION The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.
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Affiliation(s)
- Katharina A Heger
- Department of Ophthalmology, Landesklinikum Mistelbach-Gaenserndorf, Mistelbach, Austria
| | - Sebastian M Waldstein
- Department of Ophthalmology, Landesklinikum Mistelbach-Gaenserndorf, Mistelbach, Austria
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14
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Zhang Y, Li Y, Liu J, Wang J, Li H, Zhang J, Yu X. Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis. Eye (Lond) 2023; 37:3565-3573. [PMID: 37117783 PMCID: PMC10141825 DOI: 10.1038/s41433-023-02551-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/30/2023] [Accepted: 04/18/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND/OBJECTIVE Pathologic myopia (PM) is a major cause of severe visual impairment and blindness, and current applications of artificial intelligence (AI) have covered the diagnosis and classification of PM. This meta-analysis and systematic review aimed to evaluate the overall performance of AI-based models in detecting PM and related complications. METHODS We searched PubMed, Scopus, Embase, Web of Science and IEEE Xplore for eligible studies before Dec 20, 2022. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We calculated the pooled sensitivity (SEN), specificity (SPE) and the summary area under the curve (AUC) using a random effects model, to evaluate the performance of AI in the detection of PM based on fundus or optical coherence tomography (OCT) images. RESULTS 22 studies were included in the systematic review, and 14 of them were included in the quantitative analysis. Of all included studies, SEN and SPE ranged from 80.0% to 98.7% and from 79.5% to 100.0% for PM detection, respectively. For the detection of PM, the summary AUC was 0.99 (95% confidence interval (CI) 0.97 to 0.99), and the pooled SEN and SPE were 0.95 (95% CI 0.92 to 0.96) and 0.97 (95% CI: 0.94 to 0.98), respectively. For the detection of PM-related choroid neovascularization (CNV), the summary AUC was 0.99 (95% CI: 0.97 to 0.99). CONCLUSION Our review demonstrated the excellent performance of current AI algorithms in detecting PM and related complications based on fundus and OCT images.
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Affiliation(s)
- Yue Zhang
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Yilin Li
- Center for Statistical Sciences, Peking University, Beijing, China
| | - Jing Liu
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
| | - Jianing Wang
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hui Li
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Jinrong Zhang
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaobing Yu
- Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
- Graduate School of Peking Union Medical College, Beijing, China.
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15
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Chaitanuwong P, Singhanetr P, Chainakul M, Arjkongharn N, Ruamviboonsuk P, Grzybowski A. Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies. Neurol Ther 2023; 12:1517-1532. [PMID: 37468682 PMCID: PMC10444735 DOI: 10.1007/s40120-023-00526-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.
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Affiliation(s)
- Pareena Chaitanuwong
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Panisa Singhanetr
- Mettapracharak Eye Institute, Mettapracharak (Wat Rai Khing) Hospital, Nakhon Pathom, Thailand
| | - Methaphon Chainakul
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Niracha Arjkongharn
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Paisan Ruamviboonsuk
- Ophthalmology Department, Rajavithi Hospital, Ministry of Public Health, Bangkok, Thailand
- Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand
| | - Andrzej Grzybowski
- Institute of Research in Ophthalmology, Foundation for Ophthalmology Development, Mickiewicza 24/3B, 60-836, Poznan, Poland.
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Chen B, Fang XW, Wu MN, Zhu SJ, Zheng B, Liu BQ, Wu T, Hong XQ, Wang JT, Yang WH. Artificial intelligence assisted pterygium diagnosis: current status and perspectives. Int J Ophthalmol 2023; 16:1386-1394. [PMID: 37724272 PMCID: PMC10475638 DOI: 10.18240/ijo.2023.09.04] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/24/2023] [Indexed: 09/20/2023] Open
Abstract
Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.
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Affiliation(s)
- Bang Chen
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Xin-Wen Fang
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Mao-Nian Wu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Shao-Jun Zhu
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Bo Zheng
- School of Information Engineering, Huzhou University, Huzhou 313000, Zhejiang Province, China
- Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000, Zhejiang Province, China
| | - Bang-Quan Liu
- College of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo 315000, Zhejiang Province, China
| | - Tao Wu
- Huzhou Institute, Zhejiang University of Technology, Huzhou 313000, Zhejiang Province, China
| | - Xiang-Qian Hong
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Jian-Tao Wang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
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17
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Muntean GA, Marginean A, Groza A, Damian I, Roman SA, Hapca MC, Muntean MV, Nicoară SD. The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review. Diagnostics (Basel) 2023; 13:2464. [PMID: 37510207 PMCID: PMC10378064 DOI: 10.3390/diagnostics13142464] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/16/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.
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Affiliation(s)
- George Adrian Muntean
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Anca Marginean
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Adrian Groza
- Department of Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Ioana Damian
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Sara Alexia Roman
- Faculty of Medicine, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania
| | - Mădălina Claudia Hapca
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
| | - Maximilian Vlad Muntean
- Plastic Surgery Department, "Prof. Dr. I. Chiricuta" Institute of Oncology, 400015 Cluj-Napoca, Romania
| | - Simona Delia Nicoară
- Department of Ophthalmology, "Iuliu Hatieganu" University of Medicine and Pharmacy, Emergency County Hospital, 400347 Cluj-Napoca, Romania
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Wawer Matos PA, Reimer RP, Rokohl AC, Caldeira L, Heindl LM, Große Hokamp N. Artificial Intelligence in Ophthalmology - Status Quo and Future Perspectives. Semin Ophthalmol 2023; 38:226-237. [PMID: 36356300 DOI: 10.1080/08820538.2022.2139625] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
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Affiliation(s)
| | - Robert P Reimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Alexander C Rokohl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Ludwig M Heindl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
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Rossi SL, Subramanian P, Bovenkamp DE. The future is precision medicine-guided diagnoses, preventions and treatments for neurodegenerative diseases. Front Aging Neurosci 2023; 15:1128619. [PMID: 37009453 PMCID: PMC10065404 DOI: 10.3389/fnagi.2023.1128619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/20/2023] [Indexed: 03/19/2023] Open
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20
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Domínguez C, Heras J, Mata E, Pascual V, Royo D, Zapata MÁ. Binary and multi-class automated detection of age-related macular degeneration using convolutional- and transformer-based architectures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107302. [PMID: 36528999 DOI: 10.1016/j.cmpb.2022.107302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 09/05/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Age-related macular degeneration (AMD) is an eye disease that happens when ageing causes damage to the macula, and it is the leading cause of blindness in developed countries. Screening retinal fundus images allows ophthalmologists to early detect, diagnose and treat this disease; however, the manual interpretation of images is a time-consuming task. In this paper, we aim to study different deep learning methods to diagnose AMD. METHODS We have conducted a thorough study of two families of deep learning models based on convolutional neural networks (CNN) and transformer architectures to automatically diagnose referable/non-referable AMD, and grade AMD severity scales (no AMD, early AMD, intermediate AMD, and advanced AMD). In addition, we have analysed several progressive resizing strategies and ensemble methods for convolutional-based architectures to further improve the performance of the models. RESULTS As a first result, we have shown that transformer-based architectures obtain considerably worse results than convolutional-based architectures for diagnosing AMD. Moreover, we have built a model for diagnosing referable AMD that yielded a mean F1-score (SD) of 92.60% (0.47), a mean AUROC (SD) of 97.53% (0.40), and a mean weighted kappa coefficient (SD) of 85.28% (0.91); and an ensemble of models for grading AMD severity scales with a mean accuracy (SD) of 82.55% (2.92), and a mean weighted kappa coefficient (SD) of 84.76% (2.45). CONCLUSIONS This work shows that working with convolutional based architectures is more suitable than using transformer based models for classifying and grading AMD from retinal fundus images. Furthermore, convolutional models can be improved by means of progressive resizing strategies and ensemble methods.
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Affiliation(s)
- César Domínguez
- Department of Mathematics and Computer Science, University of La Rioja, Spain
| | - Jónathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, Spain.
| | - Eloy Mata
- Department of Mathematics and Computer Science, University of La Rioja, Spain
| | - Vico Pascual
- Department of Mathematics and Computer Science, University of La Rioja, Spain
| | | | - Miguel Ángel Zapata
- UPRetina, Barcelona, Spain; Hospital Vall Hebron, Passeig Roser 126, Sant Cugat del Vallés, 08195 Barcelona, Spain
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Anton N, Doroftei B, Curteanu S, Catãlin L, Ilie OD, Târcoveanu F, Bogdănici CM. Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions. Diagnostics (Basel) 2022; 13:100. [PMID: 36611392 PMCID: PMC9818832 DOI: 10.3390/diagnostics13010100] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/12/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. MATERIALS AND METHODS We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. RESULTS Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. CONCLUSION In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.
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Affiliation(s)
- Nicoleta Anton
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No 16, 700115 Iasi, Romania
| | - Bogdan Doroftei
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No 16, 700115 Iasi, Romania
| | - Silvia Curteanu
- Department of Chemical Engineering, Cristofor Simionescu Faculty of Chemical Engineering and Environmental Protection, Gheorghe Asachi Technical University, Prof.dr.doc Dimitrie Mangeron Avenue, No 67, 700050 Iasi, Romania
| | - Lisa Catãlin
- Department of Chemical Engineering, Cristofor Simionescu Faculty of Chemical Engineering and Environmental Protection, Gheorghe Asachi Technical University, Prof.dr.doc Dimitrie Mangeron Avenue, No 67, 700050 Iasi, Romania
| | - Ovidiu-Dumitru Ilie
- Department of Biology, Faculty of Biology, “Alexandru Ioan Cuza” University, Carol I Avenue, No 20A, 700505 Iasi, Romania
| | - Filip Târcoveanu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No 16, 700115 Iasi, Romania
| | - Camelia Margareta Bogdănici
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No 16, 700115 Iasi, Romania
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22
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Doǧan V, Isık T, Kılıç V, Horzum N. A field-deployable water quality monitoring with machine learning-based smartphone colorimetry. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3458-3466. [PMID: 36000587 DOI: 10.1039/d2ay00785a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Water quality monitoring is an increasing global concern as the pollution of water sources causes adverse effects on economic growth and human health. Traditional approaches to the detection of pollutants are time-consuming and labor-intensive due to the requirement of sophisticated equipment or laboratory settings. Therefore, portable devices featuring rapid response and easy operation are indispensable in water quality monitoring. Herein, smartphone-based colorimetric pollutant quantification is demonstrated in a machine learning (ML) framework. As a proof of concept, the presence of seven ions in water was analyzed using colorimetric strips. The color variation on the strip indicators was captured under eight lighting conditions with five smartphones, providing robustness against the illumination variation and camera optics for ML classifiers. Color and texture features were extracted from the images to train the classifiers. Among the twenty-three classifiers, K-Nearest Neighbors exhibits the best classification performance, leading to the integration with our custom-designed Android application called Hydro Sens. The proposed approach was also tested with real samples taken from local water sources. The results prove that incorporating color strips with ML with a smartphone application can be used for water quality monitoring, which offers promising alternatives for sophisticated equipment that is especially applicable in resource-limited settings.
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Affiliation(s)
- Vakkas Doǧan
- Department of Electrical and Electronics Engineering, Izmir Katip Celebi University, 35620 Turkey.
| | - Tuǧba Isık
- Department of Mineral Analysis and Technologies, General Directorate of Mineral Research and Exploration (MTA), Ankara, Turkey
| | - Volkan Kılıç
- Department of Electrical and Electronics Engineering, Izmir Katip Celebi University, 35620 Turkey.
| | - Nesrin Horzum
- Department of Engineering Sciences, Izmir Katip Celebi University, 35620 Izmir, Turkey
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23
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Sreejith Kumar AJ, Chong RS, Crowston JG, Chua J, Bujor I, Husain R, Vithana EN, Girard MJA, Ting DSW, Cheng CY, Aung T, Popa-Cherecheanu A, Schmetterer L, Wong D. Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma. JAMA Ophthalmol 2022; 140:974-981. [PMID: 36048435 PMCID: PMC9437828 DOI: 10.1001/jamaophthalmol.2022.3375] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection. Objective To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection. Design, Setting, and Participants Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively. Main Outcomes and Measures Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications. Results A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images. Conclusions and Relevance DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.
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Affiliation(s)
- Ashish Jith Sreejith Kumar
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,Institute for Infocomm Research, A*STAR, Singapore
| | - Rachel S Chong
- Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Jonathan G Crowston
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Inna Bujor
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Rahat Husain
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Eranga N Vithana
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Michaël J A Girard
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alina Popa-Cherecheanu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,Emergency University Hospital, Department of Ophthalmology, Bucharest, Romania
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.,Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore.,Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria.,Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
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24
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Pucchio A, Krance SH, Pur DR, Miranda RN, Felfeli T. Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review. Clin Ophthalmol 2022; 16:2463-2476. [PMID: 35968055 PMCID: PMC9369085 DOI: 10.2147/opth.s377262] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 07/26/2022] [Indexed: 11/23/2022] Open
Abstract
This systematic review explores the use of artificial intelligence (AI) in the analysis of biofluid markers in age-related macular degeneration (AMD). We detail the accuracy and validity of AI in diagnostic and prognostic models and biofluid markers that provide insight into AMD pathogenesis and progression. This review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines. A comprehensive search was conducted across 5 electronic databases including Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, Medline, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics in AMD were included. Identified studies were assessed for risk of bias and critically appraised using the Joanna Briggs Institute Critical Appraisal tools. A total of 10,264 articles were retrieved from all databases and 37 studies met the inclusion criteria, including 15 cross-sectional studies, 15 prospective cohort studies, five retrospective cohort studies, one randomized controlled trial, and one case–control study. The majority of studies had a general focus on AMD (58%), while neovascular AMD (nAMD) was the focus in 11 studies (30%), and geographic atrophy (GA) was highlighted by three studies. Fifteen studies examined disease characteristics, 15 studied risk factors, and seven guided treatment decisions. Altered lipid metabolism (HDL-cholesterol, total serum triglycerides), inflammation (c-reactive protein), oxidative stress, and protein digestion were implicated in AMD development and progression. AI tools were able to both accurately differentiate controls and AMD patients with accuracies as high as 87% and predict responsiveness to anti-VEGF therapy in nAMD patients. Use of AI models such as discriminant analysis could inform prognostic and diagnostic decision-making in a clinical setting. The identified pathways provide opportunity for future studies of AMD development and could be valuable in the advancement of novel treatments.
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Affiliation(s)
- Aidan Pucchio
- School of Medicine, Queen’s University, Kingston, ON, Canada
| | - Saffire H Krance
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Daiana R Pur
- Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
| | - Rafael N Miranda
- Toronto Health Economics and Technology Assessment Collaborative, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Tina Felfeli
- Toronto Health Economics and Technology Assessment Collaborative, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada
- Correspondence: Tina Felfeli, Department of Ophthalmology and Vision Sciences, University of Toronto, 340 College Street, Suite 400, Toronto, ON, M5T 3A9, Canada, Fax +416-978-4590, Email
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25
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Yao Z, Yuan Y, Shi Z, Mao W, Zhu G, Zhang G, Wang Z. FunSwin: A deep learning method to analysis diabetic retinopathy grade and macular edema risk based on fundus images. Front Physiol 2022; 13:961386. [PMID: 35957992 PMCID: PMC9358036 DOI: 10.3389/fphys.2022.961386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are forms of degenerative retinal disorders that may result in vision impairment or even permanent blindness. Early detection of these conditions is essential to maintaining a patient’s quality of life. The fundus photography technique is non-invasive, safe, and rapid way of assessing the function of the retina. It is widely used as a diagnostic tool for patients who suffer from fundus-related diseases. Using fundus images to analyze these two diseases is a challenging exercise, since there are rarely obvious features in the images during the incipient stages of the disease. In order to deal with these issues, we have proposed a deep learning method called FunSwin. The Swin Transformer constitutes the main framework for this method. Additionally, due to the characteristics of medical images, such as their small number and relatively fixed structure, transfer learning strategy that are able to increase the low-level characteristics of the model as well as data enhancement strategy to balance the data are integrated. Experiments have demonstrated that the proposed method outperforms other state-of-the-art approaches in both binary and multiclass classification tasks on the benchmark dataset.
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Affiliation(s)
- Zhaomin Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China
| | - Yizhe Yuan
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
| | - Zhenning Shi
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
| | - Wenxin Mao
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
| | - Gancheng Zhu
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China
| | - Guoxu Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China
- *Correspondence: Guoxu Zhang, ; Zhiguo Wang,
| | - Zhiguo Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China
- *Correspondence: Guoxu Zhang, ; Zhiguo Wang,
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26
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Lu HC, Chen HY, Huang CJ, Chu PH, Wu LS, Tsai CY. Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms. Front Med (Lausanne) 2022; 9:850284. [PMID: 35836947 PMCID: PMC9273745 DOI: 10.3389/fmed.2022.850284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/25/2022] [Indexed: 02/03/2023] Open
Abstract
PurposeWe formulated and tested ensemble learning models to classify axial length (AXL) from choroidal thickness (CT) as indicated on fovea-centered, 2D single optical coherence tomography (OCT) images.DesignRetrospective cross-sectional study.ParticipantsWe analyzed 710 OCT images from 355 eyes of 188 patients. Each eye had 2 OCT images.MethodsThe CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. This study trained and validated the models to classify the AXLs eyes based on binary (AXL < or > 26 mm) and multiclass (AXL < 22 mm, between 22 and 26 mm, and > 26 mm) classifications.ResultsNo features were redundant or duplicated after an analysis using Pearson’s correlation coefficient, LASSO-Pattern search algorithm, and variance inflation factors. Among the positions, CT at the nasal side had the highest correlation with AXL followed by the central area. In binary classification, our classifiers obtained high accuracy, as indicated by accuracy, recall, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under ROC curve (AUC) values of 94.37, 100, 90.91, 100, 86.67, and 95.61%, respectively. In multiclass classification, our classifiers were also highly accurate, as indicated by accuracy, weighted recall, weighted PPV, weighted NPV, weighted F1 score, and macro AUC of 88.73, 88.73, 91.21, 85.83, 87.42, and 93.42%, respectively.ConclusionsOur binary and multiclass classifiers classify AXL well from CT, as indicated on OCT images. We demonstrated the effectiveness of the proposed classifiers and provided an assistance tool for physicians.
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Affiliation(s)
- Hao-Chun Lu
- Graduate Institute of Business and Management, Chang Gung University, Taoyuan, Taiwan
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Hsin-Yi Chen
- Department of Ophthalmology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chien-Jung Huang
- Department of Ophthalmology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan
| | - Pao-Hsien Chu
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taipei, Taiwan
| | - Lung-Sheng Wu
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taipei, Taiwan
| | - Chia-Ying Tsai
- Department of Ophthalmology, Fu Jen Catholic University Hospital, New Taipei City, Taiwan
- School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- *Correspondence: Chia-Ying Tsai,
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27
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Luo J, Chen Y, Yang Y, Zhang K, Liu Y, Zhao H, Dong L, Xu J, Li Y, Wei W. Prognosis Prediction of Uveal Melanoma After Plaque Brachytherapy Based on Ultrasound With Machine Learning. Front Med (Lausanne) 2022; 8:777142. [PMID: 35127747 PMCID: PMC8816318 DOI: 10.3389/fmed.2021.777142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/22/2021] [Indexed: 12/29/2022] Open
Abstract
INTRODUCTION Uveal melanoma (UM) is the most common intraocular malignancy in adults. Plaque brachytherapy remains the dominant eyeball-conserving therapy for UM. Tumor regression in UM after plaque brachytherapy has been reported as a valuable prognostic factor. The present study aimed to develop an accurate machine-learning model to predict the 4-year risk of metastasis and death in UM based on ocular ultrasound data. MATERIAL AND METHODS A total of 454 patients with UM were enrolled in this retrospective, single-center study. All patients were followed up for at least 4 years after plaque brachytherapy and underwent ophthalmologic evaluations before the therapy. B-scan ultrasonography was used to measure the basal diameters and thickness of tumors preoperatively and postoperatively. Random Forest (RF) algorithm was used to construct two prediction models: whether a patient will survive for more than 4 years and whether the tumor will develop metastasis within 4 years after treatment. RESULTS Our predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.708 for predicting death using only a one-time follow-up record. Including the data from two additional follow-ups increased the AUC of the model to 0.883. We attained AUCs of 0.730 and 0.846 with data from one and three-time follow-up, respectively, for predicting metastasis. The model found that the amount of postoperative follow-up data significantly improved death and metastasis prediction accuracy. Furthermore, we divided tumor treatment response into four patterns. The D(decrease)/S(stable) patterns are associated with a significantly better prognosis than the I(increase)/O(other) patterns. CONCLUSIONS The present study developed an RF model to predict the risk of metastasis and death from UM within 4 years based on ultrasound follow-up records following plaque brachytherapy. We intend to further validate our model in prospective datasets, enabling us to implement timely and efficient treatments.
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Affiliation(s)
- Jingting Luo
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yuning Chen
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yuhang Yang
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- InferVision Healthcare Science and Technology Limited Company, Shanghai, China
| | - Yueming Liu
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Hanqing Zhao
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Li Dong
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jie Xu
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yang Li
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wenbin Wei
- Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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28
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Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics (Basel) 2022; 12:diagnostics12010134. [PMID: 35054301 PMCID: PMC8774893 DOI: 10.3390/diagnostics12010134] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/03/2022] [Accepted: 01/03/2022] [Indexed: 02/04/2023] Open
Abstract
Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.
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29
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Takhchidi K, Gliznitsa PV, Svetozarskiy SN, Bursov AI, Shusterzon KA. Labelling of data on fundus color pictures used to train a deep learning model enhances its macular pathology recognition capabilities. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2021. [DOI: 10.24075/brsmu.2021.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Retinal diseases remain one of the leading causes of visual impairments in the world. The development of automated diagnostic methods can improve the efficiency and availability of the macular pathology mass screening programs. The objective of this work was to develop and validate deep learning algorithms detecting macular pathology (age-related macular degeneration, AMD) based on the analysis of color fundus photographs with and without data labeling. We used 1200 color fundus photographs from local databases, including 575 retinal images of AMD patients and 625 pictures of the retina of healthy people. The deep learning algorithm was deployed in the Faster RCNN neural network with ResNet50 for convolution. The process employed the transfer learning method. As a result, in the absence of labeling, the accuracy of the model was unsatisfactory (79%) because the neural network selected the areas of attention incorrectly. Data labeling improved the efficacy of the developed method: with the test dataset, the model determined the areas with informative features adequately, and the classification accuracy reached 96.6%. Thus, image data labeling significantly improves the accuracy of retinal color images recognition by a neural network and enables development and training of effective models with limited datasets.
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Affiliation(s)
- KhP Takhchidi
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - PV Gliznitsa
- OOO Innovatsioonniye Tekhnologii (Innovative Technologies, LLC), Nizhny Novgorod, Russia
| | - SN Svetozarskiy
- Volga District Medical Center under the Federal Medical-Biological Agency, Nizhny Novgorod, Russia
| | - AI Bursov
- Ivannikov Institute for System Programming of RAS, Moscow, Russia
| | - KA Shusterzon
- L.A. Melentiev Energy Systems Institute, Irkutsk, Russia
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