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Lavric A, Beguni C, Zadobrischi E, Căilean AM, Avătămăniței SA. A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations. SENSORS (BASEL, SWITZERLAND) 2024; 24:4834. [PMID: 39123881 PMCID: PMC11314945 DOI: 10.3390/s24154834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/01/2024] [Accepted: 07/23/2024] [Indexed: 08/12/2024]
Abstract
In the context in which severe visual impairment significantly affects human life, this article emphasizes the potential of Artificial Intelligence (AI) and Visible Light Communications (VLC) in developing future assistive technologies. Toward this path, the article summarizes the features of some commercial assistance solutions, and debates the characteristics of VLC and AI, emphasizing their compatibility with blind individuals' needs. Additionally, this work highlights the AI potential in the efficient early detection of eye diseases. This article also reviews the existing work oriented toward VLC integration in blind persons' assistive applications, showing the existing progress and emphasizing the high potential associated with VLC use. In the end, this work provides a roadmap toward the development of an integrated AI-based VLC assistance solution for visually impaired people, pointing out the high potential and some of the steps to follow. As far as we know, this is the first comprehensive work which focuses on the integration of AI and VLC technologies in visually impaired persons' assistance domain.
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Affiliation(s)
- Alexandru Lavric
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
| | - Cătălin Beguni
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
| | - Eduard Zadobrischi
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
| | - Alin-Mihai Căilean
- Department of Computers, Electronics and Automation, Stefan cel Mare University of Suceava, 720229 Suceava, Romania; (A.L.); (C.B.); (E.Z.)
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
| | - Sebastian-Andrei Avătămăniței
- Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies and Distributed Systems for Fabrication and Control, Stefan cel Mare University of Suceava, 720229 Suceava, Romania;
- East European Border Scientific and Technological Park, 725500 Siret, Romania
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Gurnani B, Kaur K, Lalgudi VG, Kundu G, Mimouni M, Liu H, Jhanji V, Prakash G, Roy AS, Shetty R, Gurav JS. Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review. J Fr Ophtalmol 2024; 47:104242. [PMID: 39013268 DOI: 10.1016/j.jfo.2024.104242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024]
Abstract
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
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Affiliation(s)
- B Gurnani
- Department of Cataract, Cornea, External Disease, Trauma, Ocular Surface and Refractive Surgery, ASG Eye Hospital, Jodhpur, Rajasthan, India.
| | - K Kaur
- Department of Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, Rajasthan, India
| | - V G Lalgudi
- Department of Cornea, Refractive surgery, Ira G Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, USA
| | - G Kundu
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - M Mimouni
- Department of Ophthalmology, Rambam Health Care Campus affiliated with the Bruce and Ruth Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - H Liu
- Department of Ophthalmology, University of Ottawa Eye Institute, Ottawa, Canada
| | - V Jhanji
- UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - G Prakash
- Department of Ophthalmology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - A S Roy
- Narayana Nethralaya Foundation, Bangalore, India
| | - R Shetty
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - J S Gurav
- Department of Opthalmology, Armed Forces Medical College, Pune, India
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Mohr N, Kassumeh S, Luft N, Dirisamer M, Priglinger SG, Mayer WJ. Enhancing ectasia screening using advanced AS-OCT: a case series of challenging refractive candidates. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1405443. [PMID: 38984129 PMCID: PMC11182286 DOI: 10.3389/fopht.2024.1405443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/16/2024] [Indexed: 07/11/2024]
Abstract
Purpose Ectasia screening in candidates for laser refractive surgery is mandatory during preoperative evaluation. Despite the availability of modern imaging techniques, refractive surgeons often face borderline decisions when patients present with suspicious tomographic findings. This case series presents refractive candidates with suspicious tomographic findings and demonstrates how to interpret them using Scheimpflug imaging and additional anterior segment optical coherence tomography (AS-OCT). Setting Department of Ophthalmology, University Hospital, LMU Munich. Case series This case series examines six potential candidates for refractive surgery with a mean age of 29.2 ± 3.9 years, whose corneal assessments using Scheimpflug imaging raised suspicion for ectasia. Each candidate was additionally examined with AS-OCT and reevaluated. The mean manifest subjective spherical equivalent was -3.67 ± 1.8 diopters. The total corneal thickness measured 537 µm ± 30 µm at its thinnest point. None of the candidates had any reported underlying corneal or ophthalmic diseases, and slit lamp examinations revealed no abnormal morphological findings. Conclusions Both Scheimpflug imaging and AS-OCT are appropriate tools for screening refractive candidates for ectasia. While topographic and elevation analyses yielded comparable results regarding corneal structure, the epithelial mapping provided by AS-OCT played a critical role in decision-making for cases with borderline tomographic findings. Establishing a global consensus on the use of epithelial mapping in ectasia screening is necessary.
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Affiliation(s)
- Niklas Mohr
- Department of Ophthalmology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Stefan Kassumeh
- Department of Ophthalmology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Nikolaus Luft
- Department of Ophthalmology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Smile Eyes Clinic, Linz, Austria
| | - Martin Dirisamer
- Department of Ophthalmology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
- Smile Eyes Clinic, Linz, Austria
| | - Siegfried G. Priglinger
- Department of Ophthalmology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Wolfgang J. Mayer
- Department of Ophthalmology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
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Goodman D, Zhu AY. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1380701. [PMID: 38984114 PMCID: PMC11182163 DOI: 10.3389/fopht.2024.1380701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/11/2024]
Abstract
Introduction The application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias. Methods We conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings. Results Ninety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus. Discussion Algorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.
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Huo Y, Chen X, Xie R, Li J, Wang Y. Longitudinal Analysis of Corneal Biomechanics of Suspect Keratoconus: A Prospective Case-Control Study. Bioengineering (Basel) 2024; 11:420. [PMID: 38790289 PMCID: PMC11118031 DOI: 10.3390/bioengineering11050420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/11/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND To evaluate the corneal biomechanics of stable keratoconus suspects (Stable-KCS) at 1-year follow-up and compare them with those of subclinical keratoconus (SKC). METHODS This prospective case-control study included the eyes of 144 patients. Biomechanical and tomographic parameters were recorded (Corvis ST and Pentacam). Patients without clinical signs of keratoconus in both eyes but suspicious tomography findings were included in the Stable-KCS group (n = 72). Longitudinal follow-up was used to evaluate Stable-KCS changes. Unilateral keratoconus contralateral eyes with suspicious tomography were included in the SKC group (n = 72). T-tests and non-parametric tests were used for comparison. Multivariate general linear models were used to adjust for confounding factors for further analysis. Receiver operating characteristic (ROC) curves were used to analyze the distinguishability. RESULTS The biomechanical and tomographic parameters of Stable-KCS showed no progression during the follow-up time (13.19 ± 2.41 months, p > 0.05). Fifteen biomechanical parameters and the Stress-Strain Index (SSI) differed between the two groups (p < 0.016). The A1 dArc length showed the strongest distinguishing ability (area under the ROC = 0.888) between Stable-KCS and SKC, with 90.28% sensitivity and 77.78% specificity at the cut-off value of -0.0175. CONCLUSIONS The A1 dArc length could distinguish between Stable-KCS and SKC, indicating the need to focus on changes in the A1 dArc length for keratoconus suspects during the follow-up period. Although both have abnormalities on tomography, the corneal biomechanics and SSI of Stable-KCS were stronger than those of SKC, which may explain the lack of progression of Stable-KCS.
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Affiliation(s)
- Yan Huo
- School of Medicine, Nankai University, Tianjin 300071, China; (Y.H.); (X.C.); (R.X.)
| | - Xuan Chen
- School of Medicine, Nankai University, Tianjin 300071, China; (Y.H.); (X.C.); (R.X.)
| | - Ruisi Xie
- School of Medicine, Nankai University, Tianjin 300071, China; (Y.H.); (X.C.); (R.X.)
| | - Jing Li
- School of Medicine, Northwest University, Xi’an 710199, China;
| | - Yan Wang
- School of Medicine, Nankai University, Tianjin 300071, China; (Y.H.); (X.C.); (R.X.)
- Clinical College of Ophthalmology, Tianjin Medical University, Tianjin 300070, China
- Nankai Eye Institute, Nankai University, Tianjin 300071, China
- Tianjin Eye Hospital, Tianjin Key Lab of Ophthalmology and Visual Science, Tianjin Eye Institute, Nankai University Affiliated Eye Hospital, Tianjin 300020, China
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Hashemi H, Doroodgar F, Niazi S, Khabazkhoob M, Heidari Z. Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis. Graefes Arch Clin Exp Ophthalmol 2024; 262:1017-1039. [PMID: 37418053 DOI: 10.1007/s00417-023-06154-6] [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: 11/12/2022] [Revised: 04/18/2023] [Accepted: 06/16/2023] [Indexed: 07/08/2023] Open
Abstract
PURPOSE This review was designed to compare different corneal imaging modalities using artificial intelligence (AI) for the diagnosis of keratoconus (KCN), subclinical KCN (SKCN), and forme fruste KCN (FFKCN). METHODS A comprehensive systematic search was conducted in scientific databases, including Web of Science, PubMed, Scopus, and Google Scholar based on the PRISMA statement. Two independent reviewers assessed all potential publications on AI and KCN up to March 2022. The Critical Appraisal Skills Program (CASP) 11-item checklist was used to evaluate the validity of the studies. Eligible articles were categorized into three groups (KCN, SKCN, and FFKCN) and included in the meta-analysis. The pooled estimate of accuracy (PEA) was calculated for all selected articles. RESULTS The initial search yielded 575 relevant publications, of which 36 met the CASP quality criteria and were included in the analysis. Qualitative assessment showed that Scheimpflug and Placido combined with biomechanical and wavefront evaluations improved KCN detection (PEA, 99.2, and 99.0, respectively). The Scheimpflug system (92.25 PEA, 95% CI, 94.76-97.51) and a combination of Scheimpflug and Placido (96.44 PEA, 95% CI, 93.13-98.19) had the highest diagnostic accuracy for the detection of SKCN and FFKCN, respectively. The meta-analysis outcomes showed no significant difference between the CASP score and accuracy of the publications (all P > 0.05). CONCLUSIONS Simultaneous Scheimpflug and Placido corneal imaging methods provide high diagnostic accuracy for early detection of keratoconus. The use of AI models improves the discrimination of keratoconic eyes from normal corneas.
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Affiliation(s)
- Hassan Hashemi
- Noor Research Center for Ophthalmic Epidemiology, Noor Eye Hospital, Tehran, Iran
| | - Farideh Doroodgar
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Negah Eye Hospital Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sana Niazi
- Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mehdi Khabazkhoob
- Department of Medical Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Heidari
- Department of Ophthalmology, Bu-Ali Sina Hospital, Mazandaran University of Medical Sciences, Sari, Iran.
- Psychiatry and Behavioral Sciences Research Center, Mazandaran University of Medical Sciences, Sari, Iran.
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Bhattacharya P, Edwards K, Schmid KL. Regional Variations in Corneal Epithelial Cell Density and Morphology Assessed Using In Vivo Confocal Microscopy. Eye Contact Lens 2024; 50:163-170. [PMID: 38350097 DOI: 10.1097/icl.0000000000001067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 02/15/2024]
Abstract
AIM To characterize the regional variations in corneal epithelial cell density and morphology using in vivo confocal microscopy (IVCM). METHODS Corneal imaging (IVCM) at 10 locations was performed; corneal apex (i.e., the center), immediately anterior to the corneal nerve whorl (i.e., slightly inferior to the apex), and four quadrants (superior, nasal, inferior, and temporal) both at 1.5 mm and 4.5 mm from the corneal apex (corresponding to 3 mm central and 9 mm peripheral diameter rings). The data of 21 young adults, aged 18 to 35 years, were analyzed. Cell morphometric parameters, including cell density, area, perimeter, Feret diameter, and circularity, were measured for basal and wing cells using Image J software. RESULTS There was a significant difference in basal cell density (BCD) ( P <0.001) and wing cell density (WCD) ( P <0.001) for different corneal locations. The BCD (mean±SD: 8,839±416 cells/mm 2 ) and WCD (mean±SD: 5,932±310 cells/mm 2 ) were highest at the corneal nerve whorl compared with all other corneal locations. There were significant differences in wing cell area ( P <0.001), perimeter ( P <0.001), Feret diameter ( P <0.001), and circularity ( P <0.001) at varying corneal locations. CONCLUSION There are significant regional variations in corneal epithelial cell density and morphology. The BCD and WCD was highest anterior to the corneal nerve whorl.
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Affiliation(s)
- Pradipta Bhattacharya
- Centre for Vision and Eye Research, Faculty of Health, Queensland University of Technology, Kelvin Grove, Australia
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Bodmer NS, Christensen DG, Bachmann LM, Faes L, Sanak F, Iselin K, Kaufmann C, Thiel MA, Baenninger PB. Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis. Cornea 2024; 43:00003226-990000000-00474. [PMID: 38300179 PMCID: PMC11142647 DOI: 10.1097/ico.0000000000003467] [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: 04/05/2023] [Revised: 10/10/2023] [Accepted: 11/26/2023] [Indexed: 02/02/2024]
Abstract
PURPOSE The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for improving the accuracy of keratoconus detection. Deep learning (DL) offers considerable promise for improving the accuracy and speed of medical imaging interpretation. We establish an inventory of studies conducted with DL algorithms that have attempted to diagnose keratoconus. METHODS This systematic review was conducted according to the recommendations of the PRISMA statement. We searched (Pre-)MEDLINE, Embase, Science Citation Index, Conference Proceedings Citation Index, arXiv document server, and Google Scholar from inception to February 18, 2022. We included studies that evaluated the performance of DL algorithms in the diagnosis of keratoconus. The main outcome was diagnostic performance measured as sensitivity and specificity, and the methodological quality of the included studies was assessed using QUADAS-2. RESULTS Searches retrieved 4100 nonduplicate records, and we included 19 studies in the qualitative synthesis and 10 studies in the exploratory meta-analysis. The overall study quality was limited because of poor reporting of patient selection and the use of inadequate reference standards. We found a pooled sensitivity of 97.5% (95% confidence interval, 93.6%-99.0%) and a pooled specificity of 97.2% (95% confidence interval, 85.7%-99.5%) for topography images as input. CONCLUSIONS Our systematic review found that the overall diagnostic performance of DL models to detect keratoconus was good, but the methodological quality of included studies was modest.
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Affiliation(s)
- Nicolas S. Bodmer
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Medignition Inc. Research Consultants Zurich, Zurich, Switzerland
- University of Toronto, Institute of Health Policy, Management and Evaluation (IHPME), Toronto, ON, Canada;
| | | | - Lucas M. Bachmann
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Medignition Inc. Research Consultants Zurich, Zurich, Switzerland
| | - Livia Faes
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Medignition Inc. Research Consultants Zurich, Zurich, Switzerland
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Frantisek Sanak
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Katja Iselin
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Claude Kaufmann
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Michael A. Thiel
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
| | - Philipp B. Baenninger
- Medical Faculty, University of Zurich, Zurich, Switzerland
- Department of Ophthalmology, Cantonal Hospital of Lucerne, Lucerne, Switzerland; and
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Vandevenne MM, Favuzza E, Veta M, Lucenteforte E, Berendschot TT, Mencucci R, Nuijts RM, Virgili G, Dickman MM. Artificial intelligence for detecting keratoconus. Cochrane Database Syst Rev 2023; 11:CD014911. [PMID: 37965960 PMCID: PMC10646985 DOI: 10.1002/14651858.cd014911.pub2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
BACKGROUND Keratoconus remains difficult to diagnose, especially in the early stages. It is a progressive disorder of the cornea that starts at a young age. Diagnosis is based on clinical examination and corneal imaging; though in the early stages, when there are no clinical signs, diagnosis depends on the interpretation of corneal imaging (e.g. topography and tomography) by trained cornea specialists. Using artificial intelligence (AI) to analyse the corneal images and detect cases of keratoconus could help prevent visual acuity loss and even corneal transplantation. However, a missed diagnosis in people seeking refractive surgery could lead to weakening of the cornea and keratoconus-like ectasia. There is a need for a reliable overview of the accuracy of AI for detecting keratoconus and the applicability of this automated method to the clinical setting. OBJECTIVES To assess the diagnostic accuracy of artificial intelligence (AI) algorithms for detecting keratoconus in people presenting with refractive errors, especially those whose vision can no longer be fully corrected with glasses, those seeking corneal refractive surgery, and those suspected of having keratoconus. AI could help ophthalmologists, optometrists, and other eye care professionals to make decisions on referral to cornea specialists. Secondary objectives To assess the following potential causes of heterogeneity in diagnostic performance across studies. • Different AI algorithms (e.g. neural networks, decision trees, support vector machines) • Index test methodology (preprocessing techniques, core AI method, and postprocessing techniques) • Sources of input to train algorithms (topography and tomography images from Placido disc system, Scheimpflug system, slit-scanning system, or optical coherence tomography (OCT); number of training and testing cases/images; label/endpoint variable used for training) • Study setting • Study design • Ethnicity, or geographic area as its proxy • Different index test positivity criteria provided by the topography or tomography device • Reference standard, topography or tomography, one or two cornea specialists • Definition of keratoconus • Mean age of participants • Recruitment of participants • Severity of keratoconus (clinically manifest or subclinical) SEARCH METHODS: We searched CENTRAL (which contains the Cochrane Eyes and Vision Trials Register), Ovid MEDLINE, Ovid Embase, OpenGrey, the ISRCTN registry, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP). There were no date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 29 November 2022. SELECTION CRITERIA We included cross-sectional and diagnostic case-control studies that investigated AI for the diagnosis of keratoconus using topography, tomography, or both. We included studies that diagnosed manifest keratoconus, subclinical keratoconus, or both. The reference standard was the interpretation of topography or tomography images by at least two cornea specialists. DATA COLLECTION AND ANALYSIS Two review authors independently extracted the study data and assessed the quality of studies using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. When an article contained multiple AI algorithms, we selected the algorithm with the highest Youden's index. We assessed the certainty of evidence using the GRADE approach. MAIN RESULTS We included 63 studies, published between 1994 and 2022, that developed and investigated the accuracy of AI for the diagnosis of keratoconus. There were three different units of analysis in the studies: eyes, participants, and images. Forty-four studies analysed 23,771 eyes, four studies analysed 3843 participants, and 15 studies analysed 38,832 images. Fifty-four articles evaluated the detection of manifest keratoconus, defined as a cornea that showed any clinical sign of keratoconus. The accuracy of AI seems almost perfect, with a summary sensitivity of 98.6% (95% confidence interval (CI) 97.6% to 99.1%) and a summary specificity of 98.3% (95% CI 97.4% to 98.9%). However, accuracy varied across studies and the certainty of the evidence was low. Twenty-eight articles evaluated the detection of subclinical keratoconus, although the definition of subclinical varied. We grouped subclinical keratoconus, forme fruste, and very asymmetrical eyes together. The tests showed good accuracy, with a summary sensitivity of 90.0% (95% CI 84.5% to 93.8%) and a summary specificity of 95.5% (95% CI 91.9% to 97.5%). However, the certainty of the evidence was very low for sensitivity and low for specificity. In both groups, we graded most studies at high risk of bias, with high applicability concerns, in the domain of patient selection, since most were case-control studies. Moreover, we graded the certainty of evidence as low to very low due to selection bias, inconsistency, and imprecision. We could not explain the heterogeneity between the studies. The sensitivity analyses based on study design, AI algorithm, imaging technique (topography versus tomography), and data source (parameters versus images) showed no differences in the results. AUTHORS' CONCLUSIONS AI appears to be a promising triage tool in ophthalmologic practice for diagnosing keratoconus. Test accuracy was very high for manifest keratoconus and slightly lower for subclinical keratoconus, indicating a higher chance of missing a diagnosis in people without clinical signs. This could lead to progression of keratoconus or an erroneous indication for refractive surgery, which would worsen the disease. We are unable to draw clear and reliable conclusions due to the high risk of bias, the unexplained heterogeneity of the results, and high applicability concerns, all of which reduced our confidence in the evidence. Greater standardization in future research would increase the quality of studies and improve comparability between studies.
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Affiliation(s)
- Magali Ms Vandevenne
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Eleonora Favuzza
- Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
| | - Mitko Veta
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Ersilia Lucenteforte
- Department of Statistics, Computer Science and Applications «G. Parenti», University of Florence, Florence, Italy
| | - Tos Tjm Berendschot
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Rita Mencucci
- Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
| | - Rudy Mma Nuijts
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Gianni Virgili
- Department of Neurosciences, Psychology, Pharmacology and Child Health, University of Florence, Florence, Italy
- Queen's University Belfast, Belfast, UK
| | - Mor M Dickman
- University Eye Clinic Maastricht, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
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10
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Rocha-de-Lossada C, Mazzotta C, Gabrielli F, Papa FT, Gómez-Huertas C, García-López C, Urbinati F, Rachwani-Anil R, García-Lorente M, Sánchez-González JM, Rechichi M, Rubegni G, Borroni D. Ocular Surface Microbiota in Naïve Keratoconus: A Multicenter Validation Study. J Clin Med 2023; 12:6354. [PMID: 37834997 PMCID: PMC10573816 DOI: 10.3390/jcm12196354] [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: 09/10/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 10/15/2023] Open
Abstract
In the field of Ophthalmology, the mNGS 16S rRNA sequencing method of studying the microbiota and ocular microbiome is gaining more and more weight in the scientific community. This study aims to characterize the ocular microbiota of patients diagnosed with keratoconus who have not undergone any prior surgical treatment using the mNGS 16S rRNA sequencing method. Samples of naïve keratoconus patients were collected with an eNAT with 1 mL of Liquid Amies Medium (Copan Brescia, Italy), and DNA was extracted and analyzed with 16S NGS. The microbiota analysis showed a relative abundance of microorganisms at the phylum level in each sample collected from 38 patients with KC and 167 healthy controls. A comparison between healthy control and keratoconus samples identified two genera unique to keratoconus, Pelomonas and Ralstonia. Our findings suggest that alterations in the microbiota may play a role in the complex scenario of KC development.
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Affiliation(s)
- Carlos Rocha-de-Lossada
- Eyemetagenomics Ltd., 71–75, Shelton Street, Covent Garden, London WC2H 9JQ, UK;
- Ophthalmology Department, QVision, Vithas Almería, 04120 Almeria, Spain
- Ophthalmology Department, Hospital Regional Universitario Málaga, 29010 Malaga, Spain; (F.U.); (M.G.-L.)
| | - Cosimo Mazzotta
- Siena Crosslinking Center, 53100 Siena, Italy;
- Departmental Ophthalmology Unit, USL Toscana Sud Est l, 53100 Siena, Italy
- Postgraduate Ophthalmology School, University of Siena, 53100 Siena, Italy;
| | - Federico Gabrielli
- Biolab SRL, Laboratorio di Genetica e Genomica Molecolare, Largo degli Aranci, 9, 63100 Ascoli Piceno, Italy; (F.G.); (F.T.P.)
| | - Filomena Tiziana Papa
- Biolab SRL, Laboratorio di Genetica e Genomica Molecolare, Largo degli Aranci, 9, 63100 Ascoli Piceno, Italy; (F.G.); (F.T.P.)
| | - Carmen Gómez-Huertas
- Department of Ophthalmology, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain; (C.G.-H.); (C.G.-L.)
| | - Celia García-López
- Department of Ophthalmology, Hospital Universitario Virgen de las Nieves, 18014 Granada, Spain; (C.G.-H.); (C.G.-L.)
| | - Facundo Urbinati
- Ophthalmology Department, Hospital Regional Universitario Málaga, 29010 Malaga, Spain; (F.U.); (M.G.-L.)
| | | | - María García-Lorente
- Ophthalmology Department, Hospital Regional Universitario Málaga, 29010 Malaga, Spain; (F.U.); (M.G.-L.)
| | | | - Miguel Rechichi
- Centro Polispecialistico Mediterraneo, 88050 Sellia Marina, Italy;
| | - Giovanni Rubegni
- Postgraduate Ophthalmology School, University of Siena, 53100 Siena, Italy;
| | - Davide Borroni
- Eyemetagenomics Ltd., 71–75, Shelton Street, Covent Garden, London WC2H 9JQ, UK;
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11
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Shao Y, Jie Y, Liu ZG. Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023). Int J Ophthalmol 2023; 16:1373-1385. [PMID: 37724278 PMCID: PMC10475626 DOI: 10.18240/ijo.2023.09.03] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/14/2023] [Indexed: 09/20/2023] Open
Abstract
The landscape of ophthalmology has observed monumental shifts with the advent of artificial intelligence (AI) technologies. This article is devoted to elaborating on the nuanced application of AI in the diagnostic realm of anterior segment eye diseases, an area ripe with potential yet complex in its imaging characteristics. Historically, AI's entrenchment in ophthalmology was predominantly rooted in the posterior segment. However, the evolution of machine learning paradigms, particularly with the advent of deep learning methodologies, has reframed the focus. When combined with the exponential surge in available electronic image data pertaining to the anterior segment, AI's role in diagnosing corneal, conjunctival, lens, and eyelid pathologies has been solidified and has emerged from the realm of theoretical to practical. In light of this transformative potential, collaborations between the Ophthalmic Imaging and Intelligent Medicine Subcommittee of the China Medical Education Association and the Ophthalmology Committee of the International Translational Medicine Association have been instrumental. These eminent bodies mobilized a consortium of experts to dissect and assimilate advancements from both national and international quarters. Their mandate was not limited to AI's application in anterior segment pathologies like the cornea, conjunctiva, lens, and eyelids, but also ventured into deciphering the existing impediments and envisioning future trajectories. After iterative deliberations, the consensus synthesized herein serves as a touchstone, assisting ophthalmologists in optimally integrating AI into their diagnostic decisions and bolstering clinical research. Through this guideline, we aspire to offer a comprehensive framework, ensuring that clinical decisions are not merely informed but transformed by AI. By building upon existing literature yet maintaining the highest standards of originality, this document stands as a testament to both innovation and academic integrity, in line with the ethos of renowned journals such as Ophthalmology.
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Affiliation(s)
- Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Ying Jie
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University; Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing 100730, China
| | - Zu-Guo Liu
- Eye Institute of Xiamen University, Xiamen 361102, Fujian Province, China
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12
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Deshmukh R, Ong ZZ, Rampat R, Alió del Barrio JL, Barua A, Ang M, Mehta JS, Said DG, Dua HS, Ambrósio R, Ting DSJ. Management of keratoconus: an updated review. Front Med (Lausanne) 2023; 10:1212314. [PMID: 37409272 PMCID: PMC10318194 DOI: 10.3389/fmed.2023.1212314] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Keratoconus is the most common corneal ectatic disorder. It is characterized by progressive corneal thinning with resultant irregular astigmatism and myopia. Its prevalence has been estimated at 1:375 to 1:2,000 people globally, with a considerably higher rate in the younger populations. Over the past two decades, there was a paradigm shift in the management of keratoconus. The treatment has expanded significantly from conservative management (e.g., spectacles and contact lenses wear) and penetrating keratoplasty to many other therapeutic and refractive modalities, including corneal cross-linking (with various protocols/techniques), combined CXL-keratorefractive surgeries, intracorneal ring segments, anterior lamellar keratoplasty, and more recently, Bowman's layer transplantation, stromal keratophakia, and stromal regeneration. Several recent large genome-wide association studies (GWAS) have identified important genetic mutations relevant to keratoconus, facilitating the development of potential gene therapy targeting keratoconus and halting the disease progression. In addition, attempts have been made to leverage the power of artificial intelligence-assisted algorithms in enabling earlier detection and progression prediction in keratoconus. In this review, we provide a comprehensive overview of the current and emerging treatment of keratoconus and propose a treatment algorithm for systematically guiding the management of this common clinical entity.
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Affiliation(s)
- Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Zun Zheng Ong
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, United Kingdom
| | - Radhika Rampat
- Department of Ophthalmology, Royal Free London NHS Foundation Trust, London, United Kingdom
| | - Jorge L. Alió del Barrio
- Cornea, Cataract and Refractive Surgery Unit, Vissum (Miranza Group), Alicante, Spain
- Division of Ophthalmology, School of Medicine, Universidad Miguel Hernández, Alicante, Spain
| | - Ankur Barua
- Birmingham and Midland Eye Centre, Birmingham, United Kingdom
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Jodhbir S. Mehta
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Dalia G. Said
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, United Kingdom
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Harminder S. Dua
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, United Kingdom
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Renato Ambrósio
- Department of Cornea and Refractive Surgery, Instituto de Olhos Renato Ambrósio, Rio de Janeiro, Brazil
- Department of Ophthalmology, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil
- Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - Darren Shu Jeng Ting
- Birmingham and Midland Eye Centre, Birmingham, United Kingdom
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
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13
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Momeni-Moghaddam H, Naroo SA. Keratoconus detection with a focus on new indices and techniques. Cont Lens Anterior Eye 2023; 46:101851. [PMID: 37183060 DOI: 10.1016/j.clae.2023.101851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Affiliation(s)
- Hamed Momeni-Moghaddam
- Rehabilitation Sciences Research Center, Zahedan University of Medical Sciences, Zahedan, Iran.
| | - Shehzad A Naroo
- College of Health and Life Sciences, Aston University, Birmingham, UK.
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Al-Timemy AH, Alzubaidi L, Mosa ZM, Abdelmotaal H, Ghaeb NH, Lavric A, Hazarbassanov RM, Takahashi H, Gu Y, Yousefi S. A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning. Diagnostics (Basel) 2023; 13:diagnostics13101689. [PMID: 37238174 DOI: 10.3390/diagnostics13101689] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/06/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97-100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91-0.92 and an accuracy range of 88-92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.
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Affiliation(s)
- Ali H Al-Timemy
- Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10011, Iraq
| | - Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Zahraa M Mosa
- Department of Physics, College of Science, Al-Nahrain University, Baghdad 64021, Iraq
| | | | - Nebras H Ghaeb
- Biomedical Engineering Department, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad 10011, Iraq
| | - Alexandru Lavric
- Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
| | - Rossen M Hazarbassanov
- Medical School, Universidade Anhembi Morumbi, São Paulo 03101-001, Brazil
- Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of São Paulo, São Paulo 04021-001, Brazil
| | - Hidenori Takahashi
- Department of Ophthalmology, Jichi Medical University, Tochigi 329-0431, Japan
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, TN 38163, USA
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
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15
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Patefield A, Meng Y, Airaldi M, Coco G, Vaccaro S, Parekh M, Semeraro F, Gadhvi KA, Kaye SB, Zheng Y, Romano V. Deep Learning Using Preoperative AS-OCT Predicts Graft Detachment in DMEK. Transl Vis Sci Technol 2023; 12:14. [PMID: 37184500 DOI: 10.1167/tvst.12.5.14] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Purpose To evaluate a novel deep learning algorithm to distinguish between eyes that may or may not have a graft detachment based on pre-Descemet membrane endothelial keratoplasty (DMEK) anterior segment optical coherence tomography (AS-OCT) images. Methods Retrospective cohort study. A multiple-instance learning artificial intelligence (MIL-AI) model using a ResNet-101 backbone was designed. AS-OCT images were split into training and testing sets. The MIL-AI model was trained and validated on the training set. Model performance and heatmaps were calculated from the testing set. Classification performance metrics included F1 score (harmonic mean of recall and precision), specificity, sensitivity, and area under curve (AUC). Finally, MIL-AI performance was compared to manual classification by an experienced ophthalmologist. Results In total, 9466 images of 74 eyes (128 images per eye) were included in the study. Images from 50 eyes were used to train and validate the MIL-AI system, while the remaining 24 eyes were used as the test set to determine its performance and generate heatmaps for visualization. The performance metrics on the test set (95% confidence interval) were as follows: F1 score, 0.77 (0.57-0.91); precision, 0.67 (0.44-0.88); specificity, 0.45 (0.15-0.75); sensitivity, 0.92 (0.73-1.00); and AUC, 0.63 (0.52-0.86). MIL-AI performance was more sensitive (92% vs. 31%) but less specific (45% vs. 64%) than the ophthalmologist's performance. Conclusions The MIL-AI predicts with high sensitivity the eyes that may have post-DMEK graft detachment requiring rebubbling. Larger-scale clinical trials are warranted to validate the model. Translational Relevance MIL-AI models represent an opportunity for implementation in routine DMEK suitability screening.
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Affiliation(s)
- Alastair Patefield
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Yanda Meng
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Matteo Airaldi
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Giulia Coco
- Department of Corneal Diseases, St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Department of Clinical Science and Translational Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Sabrina Vaccaro
- Department of Corneal Diseases, St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Department of Ophthalmology, University of "Magna Graecia," Catanzaro, Italy
| | - Mohit Parekh
- Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Francesco Semeraro
- Ophthalmology Clinic, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Kunal A Gadhvi
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
- Department of Corneal Diseases, St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Stephen B Kaye
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
- Department of Corneal Diseases, St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Yalin Zheng
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
- Department of Corneal Diseases, St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart and Chest Hospital, Liverpool, UK
| | - Vito Romano
- Department of Eye and Vision Sciences, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
- Department of Corneal Diseases, St. Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Ophthalmology Clinic, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
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16
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Xu Z, Xu J, Shi C, Xu W, Jin X, Han W, Jin K, Grzybowski A, Yao K. Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications. Ophthalmol Ther 2023; 12:1439-1455. [PMID: 36884203 PMCID: PMC10164195 DOI: 10.1007/s40123-023-00690-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.
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Affiliation(s)
- Zhe Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Jia Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Ce Shi
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Wen Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Xiuming Jin
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Wei Han
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Kai Jin
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Ke Yao
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
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17
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Velarde-Rodriguez G, Belda-Para C, Velasco-Ocaña M, Trujillo-Sevilla JM, Rodríguez-Martin J, Jiménez-Alfaro I, Rodriguez-Ramos JM, Alejandre-Alba N. Ultra-High Resolution Optical Aberrometry in Patients with Keratoconus: A Cross-Sectional Study. Ophthalmol Ther 2023; 12:1569-1582. [PMID: 36856979 PMCID: PMC10164210 DOI: 10.1007/s40123-023-00684-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/07/2023] [Indexed: 03/02/2023] Open
Abstract
INTRODUCTION This study performs optical aberration assessment in patients using a novel ultra-high-resolution device. The objective of this study is to analyze optical aberrations, especially the very high order wavefront (more than 10th order of Zernike coefficients), and compare between keratoconus and healthy patients. METHODS In this cross-sectional study, we analyzed 43 eyes from 25 healthy patients and 43 eyes from 27 patients with keratoconus using corneal tomography and a very high-resolution (8.55 µm) aberrometer prototype (T-eyede) outfitted with a sensor originally developed for use in the field of astrophysics. Corneal aberration values were assessed using an optical model built with Zemax optical software, while ocular aberrations were assessed using T-eyede. In addition, image-processing analysis was performed of the wavefront phase, creating a high-pass filter map. RESULTS We found lower values for ocular aberrations than corneal aberrations in both groups (p < 0.001). Specifically, we found a reduction in primary astigmatism (0.145 µm) and primary coma (0.017 µm). Also, the keratoconus group showed significantly higher wavefront aberration values compared with controls (p < 0.001). An analysis of the high-pass filter map revealed 2 contrasting results: one smooth or clear, while the other presented a banding pattern. Almost all in the control group (95%) showed the first pattern, while 77% of the keratoconus group showed a banding pattern on the filtered map (chi-squared test, p < 0.001). CONCLUSION This device provides reliable, precise measurements of ocular aberrations that correlate well with corneal aberrations. Furthermore, the extraordinary high-resolution measurements revealed unprecedented micro changes in the wavefront phase of patients with keratoconus that varied with disease stage. These findings could lead to new screening or follow-up methods.
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Affiliation(s)
| | - Carolina Belda-Para
- Wooptix S.L. Avda, Trinidad 61 Planta 7, La Laguna, Tenerife, Canary Islands, Spain
| | - Miriam Velasco-Ocaña
- Wooptix S.L. Avda, Trinidad 61 Planta 7, La Laguna, Tenerife, Canary Islands, Spain
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18
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Iao WC, Zhang W, Wang X, Wu Y, Lin D, Lin H. Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13050900. [PMID: 36900043 PMCID: PMC10001234 DOI: 10.3390/diagnostics13050900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/16/2023] [Accepted: 02/18/2023] [Indexed: 03/06/2023] Open
Abstract
Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.
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Affiliation(s)
- Wai Cheng Iao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Weixing Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou 570311, China
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510060, China
- Correspondence:
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19
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Gao S, Chen Y, Shi F, Peng Y, Xu C, Chen Z, Zhu W, Xu X, Tang W, Tan Z, Xu Y, Ren Y, Zhang X, Chen X. LKG-Net: lightweight keratoconus grading network based on corneal topography. BIOMEDICAL OPTICS EXPRESS 2023; 14:799-814. [PMID: 36874500 PMCID: PMC9979687 DOI: 10.1364/boe.480564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/01/2022] [Accepted: 12/10/2022] [Indexed: 06/18/2023]
Abstract
Keratoconus (KC) is a noninflammatory ectatic disease characterized by progressive thinning and an apical cone-shaped protrusion of the cornea. In recent years, more and more researchers have been committed to automatic and semi-automatic KC detection based on corneal topography. However, there are few studies about the severity grading of KC, which is particularly important for the treatment of KC. In this work, we propose a lightweight KC grading network (LKG-Net) for 4-level KC grading (Normal, Mild, Moderate, and Severe). First of all, we use depth-wise separable convolution to design a novel feature extraction block based on the self-attention mechanism, which can not only extract rich features but also reduce feature redundancy and greatly reduce the number of parameters. Then, to improve the model performance, a multi-level feature fusion module is proposed to fuse features from the upper and lower levels to obtain more abundant and effective features. The proposed LKG-Net was evaluated on the corneal topography of 488 eyes from 281 people with 4-fold cross-validation. Compared with other state-of-the-art classification methods, the proposed method achieves 89.55% for weighted recall (W_R), 89.98% for weighted precision (W_P), 89.50% for weighted F1 score (W_F1) and 94.38% for Kappa, respectively. In addition, the LKG-Net is also evaluated on KC screening, and the experimental results show the effectiveness.
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Affiliation(s)
- Song Gao
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
- These authors contributed equally to this paper
| | - Yingjie Chen
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, China
- These authors contributed equally to this paper
| | - Fei Shi
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Yuanyuan Peng
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Chenan Xu
- State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou 215006, China
| | - Zhongyue Chen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Weifang Zhu
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Xin Xu
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Wei Tang
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Zhiwei Tan
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
| | - Yue Xu
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, China
| | - Yaru Ren
- Department of Ophthalmology, Soochow University-Affiliated First Hospital, Suzhou, Suzhou, 215006, China
| | - Xiaofeng Zhang
- Department of Ophthalmology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, 215006, China
- Department of Ophthalmology, Soochow University-Affiliated First Hospital, Suzhou, Suzhou, 215006, China
| | - Xinjian Chen
- MIPAV Lab, School of Electronics and Information Engineering, Soochow University, Suzhou, 215006, China
- State Key Laboratory of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, and School for Radiological and Interdisciplinary Sciences (RAD-X), Soochow University, Suzhou 215006, China
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20
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Zhang Z, Wang Y, Zhang H, Samusak A, Rao H, Xiao C, Abula M, Cao Q, Dai Q. Artificial intelligence-assisted diagnosis of ocular surface diseases. Front Cell Dev Biol 2023; 11:1133680. [PMID: 36875760 PMCID: PMC9981656 DOI: 10.3389/fcell.2023.1133680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/08/2023] [Indexed: 02/19/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.
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Affiliation(s)
- Zuhui Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ying Wang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Hongzhen Zhang
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Arzigul Samusak
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Huimin Rao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Chun Xiao
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Muhetaer Abula
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China
| | - Qixin Cao
- Huzhou Traditional Chinese Medicine Hospital Affiliated to Zhejiang University of Traditional Chinese Medicine, Huzhou, China
| | - Qi Dai
- The First People's Hospital of Aksu District in Xinjiang, Aksu City, China.,National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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21
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Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K, Li K, Liu Y. Advances in artificial intelligence applications for ocular surface diseases diagnosis. Front Cell Dev Biol 2022; 10:1107689. [PMID: 36605721 PMCID: PMC9808405 DOI: 10.3389/fcell.2022.1107689] [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: 11/25/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023] Open
Abstract
In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.
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Affiliation(s)
- Yuke Ji
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Sha Liu
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangqian Hong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Yi Lu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Xingyang Wu
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China
| | - Kunke Li
- Shenzhen Eye Hospital, Jinan University, Shenzhen, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Keran Li
- The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Yunfang Liu, ; Keran Li, ; Kunke Li,
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22
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Angelo L, Gokul Boptom A, McGhee C, Ziaei M. Corneal Crosslinking: Present and Future. Asia Pac J Ophthalmol (Phila) 2022; 11:441-452. [PMID: 36094381 DOI: 10.1097/apo.0000000000000557] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 07/08/2022] [Indexed: 11/25/2022] Open
Abstract
Keratoconus is a progressive corneal thinning disorder that can lead to vision loss. In the last 2 decades, corneal crosslinking (CXL) has emerged as an effective method to halt the progression of keratoconus and reduce the number of patients requiring keratoplasty. The procedure has been adopted globally and has evolved to become a part of combination treatments to regularize the cornea and improve visual outcomes. CXL has even been extrapolated in managing other ocular pathologies such as progressive myopia, infectious keratitis, and bullous keratopathy. This review aims to summarize the current role of CXL in keratoconus and its alternative uses, and provide insights into future developments in this fast-developing field.
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Affiliation(s)
- Lize Angelo
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
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23
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Abstract
PURPOSE OF REVIEW Artificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy. RECENT FINDINGS Most current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics. SUMMARY Artificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.
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Affiliation(s)
- Linda Kang
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI
| | - Dena Ballouz
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI
| | - Maria A. Woodward
- Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI
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24
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Cohen E, Bank D, Sorkin N, Giryes R, Varssano D. Use of machine learning to achieve keratoconus detection skills of a corneal expert. Int Ophthalmol 2022; 42:3837-3847. [PMID: 35953576 DOI: 10.1007/s10792-022-02404-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 06/13/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE To construct an automatic machine-learning derived algorithm discriminating between normal corneas and suspect irregular or keratoconic corneas. METHODS A total of 8526 corneal tomography images of 4904 eyes obtained between November 2010 and July 2017 using a combined Scheimpflug/Placido tomographer were retrospectively evaluated. Each image was evaluated for acquisition quality and was labeled as normal, suspect irregular or keratoconic by a cornea specialist. Two algorithms were built. The first was based on 94 instrument-derived output parameters, and the second integrated keratoconus prediction indices of the device with the 94 instrument-derived output parameters. Both models were compared with the tomographer's keratoconus detection algorithms. Out of the 8526 images evaluated, 7104 images of 3787 eyes had sufficient acquisition quality. Of those, 5904 examinations were randomly chosen for construction of the models using the random forest algorithm. The models were then validated using the remaining 1200 examinations. RESULTS Both RF algorithms had a larger AUC compared with any of the tomographer's KC detection algorithms (p < 10-9). The first constructed model had 90.2% accuracy, sensitivity of 94.2%, and specificity of 89.6% (Youden 0.838). Calculated AUC was 0.964. The second model had 91.5% accuracy, sensitivity of 94.7%, and specificity of 89.8% (Youden 0.846). Calculated AUC was 0.969. CONCLUSION Using the RF machine-learning algorithm, accuracy of discrimination between normal, suspect irregular and keratoconic corneas approximates that of an experienced corneal expert. Applying machine learning to corneal tomography can facilitate keratoconus screening in large populations as well as off-site screening of refractive surgery candidates.
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Affiliation(s)
- Eyal Cohen
- Department of Ophthalmology, Tel Aviv Sourasky Medical Center, 6 Weizmann Street, 64239, Tel Aviv, Israel. .,Faculty of Medicine, Tel Aviv University Sackler, Tel Aviv, Israel.
| | - Dor Bank
- Tel Aviv University School of Electrical Engineering, Tel Aviv, Israel
| | - Nir Sorkin
- Department of Ophthalmology, Tel Aviv Sourasky Medical Center, 6 Weizmann Street, 64239, Tel Aviv, Israel.,Faculty of Medicine, Tel Aviv University Sackler, Tel Aviv, Israel
| | - Raja Giryes
- Tel Aviv University School of Electrical Engineering, Tel Aviv, Israel
| | - David Varssano
- Department of Ophthalmology, Tel Aviv Sourasky Medical Center, 6 Weizmann Street, 64239, Tel Aviv, Israel.,Faculty of Medicine, Tel Aviv University Sackler, Tel Aviv, Israel
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Gairola S, Joshi P, Balasubramaniam A, Murali K, Kwatra N, Jain M. Keratoconus Classifier for Smartphone-based Corneal Topographer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1875-1878. [PMID: 36086067 DOI: 10.1109/embc48229.2022.9871744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Keratoconus is a severe eye disease that leads to deformation of the cornea. It impacts people aged 10-25 years and is the leading cause of blindness in that demography. Corneal topography is the gold standard for keratoconus diag-nosis. It is a non-invasive process performed using expensive and bulky medical devices called corneal topographers. This makes it inaccessible to large populations, especially in the Global South. Low-cost smartphone-based corneal topographers, such as SmartKC, have been proposed to make keratoconus diagnosis accessible. Similar to medical-grade topographers, SmartKC outputs curvature heatmaps and quantitative metrics that need to be evaluated by doctors for keratoconus diagnosis. An auto-matic scheme for evaluation of these heatmaps and quantitative values can play a crucial role in screening keratoconus in areas where doctors are not available. In this work, we propose a dual-head convolutional neural network (CNN) for classifying keratoconus on the heatmaps generated by SmartKC. Since SmartKC is a new device and only had a small dataset (114 sam-ples), we developed a 2-stage transfer learning strategy-using historical data collected from a medical-grade topographer and a subset of SmartKC data-to satisfactorily train our network. This, combined with our domain-specific data augmentations, achieved a sensitivity of 91.3% and a specificity of 94.2%.
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26
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Kamiya K, Ayatsuka Y, Kato Y, Shoji N, Mori Y, Miyata K. Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography. Front Med (Lausanne) 2021; 8:724902. [PMID: 34671618 PMCID: PMC8520919 DOI: 10.3389/fmed.2021.724902] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/06/2021] [Indexed: 01/31/2023] Open
Abstract
Purpose: Placido disk-based corneal topography is still most commonly used in daily practice. This study was aimed to evaluate the diagnosability of keratoconus using deep learning of a color-coded map with Placido disk-based corneal topography. Methods: We retrospectively examined 179 keratoconic eyes [Grade 1 (54 eyes), 2 (52 eyes), 3 (23 eyes), and 4 (50 eyes), according to the Amsler-Krumeich classification], and 170 age-matched healthy eyes, with good quality images of corneal topography measured with a Placido disk corneal topographer (TMS-4TM, Tomey). Using deep learning of a color-coded map, we evaluated the diagnostic accuracy, sensitivity, and specificity, for keratoconus screening and staging tests, in these eyes. Results: Deep learning of color-coded maps exhibited an accuracy of 0.966 (sensitivity 0.988, specificity 0.944) in discriminating keratoconus from normal eyes. It also exhibited an accuracy of 0.785 (0.911 for Grade 1, 0.868 for Grade 2, 0.920 for Grade 3, and 0.905 for Grade 4) in classifying the stage. The area under the curve value was 0.997, 0.955, 0.899, 0.888, and 0.943 as Grade 0 (normal) to 4 grading tests, respectively. Conclusions: Deep learning using color-coded maps with conventional corneal topography effectively distinguishes between keratoconus and normal eyes and classifies the grade of the disease, indicating that this will become an aid for enhancing the diagnosis and staging ability of keratoconus in a clinical setting.
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Affiliation(s)
- Kazutaka Kamiya
- Visual Physiology, School of Allied Health Sciences, Kitasato University, Kanagawa, Japan
| | | | | | - Nobuyuki Shoji
- Department of Ophthalmology, School of Medicine, Kitasato University, Kanagawa, Japan
| | - Yosai Mori
- Department of Ophthalmology, Miyata Eye Hospital, Miyazaki, Japan
| | - Kazunori Miyata
- Department of Ophthalmology, Miyata Eye Hospital, Miyazaki, Japan
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