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Wang CY, Nguyen HT, Fan WS, Lue JH, Saenprasarn P, Chen MM, Huang SY, Lin FC, Wang HC. Glaucoma Detection through a Novel Hyperspectral Imaging Band Selection and Vision Transformer Integration. Diagnostics (Basel) 2024; 14:1285. [PMID: 38928700 PMCID: PMC11202918 DOI: 10.3390/diagnostics14121285] [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: 05/20/2024] [Revised: 06/12/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
Conventional diagnostic methods for glaucoma primarily rely on non-dynamic fundus images and often analyze features such as the optic cup-to-disc ratio and abnormalities in specific retinal locations like the macula and fovea. However, hyperspectral imaging techniques focus on detecting alterations in oxygen saturation within retinal vessels, offering a potentially more comprehensive approach to diagnosis. This study explores the diagnostic potential of hyperspectral imaging for glaucoma by introducing a novel hyperspectral imaging conversion technique. Digital fundus images are transformed into hyperspectral representations, allowing for a detailed analysis of spectral variations. Spectral regions exhibiting differences are identified through spectral analysis, and images are reconstructed from these specific regions. The Vision Transformer (ViT) algorithm is then employed for classification and comparison across selected spectral bands. Fundus images are used to identify differences in lesions, utilizing a dataset of 1291 images. This study evaluates the classification performance of models using various spectral bands, revealing that the 610-780 nm band outperforms others with an accuracy, precision, recall, F1-score, and AUC-ROC all approximately at 0.9007, indicating its superior effectiveness for the task. The RGB model also shows strong performance, while other bands exhibit lower recall and overall metrics. This research highlights the disparities between machine learning algorithms and traditional clinical approaches in fundus image analysis. The findings suggest that hyperspectral imaging, coupled with advanced computational techniques such as the ViT algorithm, could significantly enhance glaucoma diagnosis. This understanding offers insights into the potential transformation of glaucoma diagnostics through the integration of hyperspectral imaging and innovative computational methodologies.
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Affiliation(s)
- Ching-Yu Wang
- Department of Ophthalmology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 62247, Taiwan; (C.-Y.W.); (W.-S.F.)
| | - Hong-Thai Nguyen
- Department of Mechanical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan;
| | - Wen-Shuang Fan
- Department of Ophthalmology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 62247, Taiwan; (C.-Y.W.); (W.-S.F.)
| | - Jiann-Hwa Lue
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Taichung City 406053, Taiwan; (J.-H.L.); (S.-Y.H.)
| | - Penchun Saenprasarn
- School of Nursing, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160, Thailand;
| | - Meei-Maan Chen
- Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan;
| | - Shuan-Yu Huang
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Taichung City 406053, Taiwan; (J.-H.L.); (S.-Y.H.)
| | - Fen-Chi Lin
- Department of Ophthalmology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Kaohsiung City 80284, Taiwan
| | - Hsiang-Chen Wang
- Department of Optometry, Central Taiwan University of Science and Technology, No. 666, Buzih Road, Taichung City 406053, Taiwan; (J.-H.L.); (S.-Y.H.)
- Hitspectra Intelligent Technology Co., Ltd., Kaohsiung City 80661, Taiwan
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Tan O, Greenfield DS, Francis BA, Varma R, Schuman JS, Huang D, Choi D. A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions. ARXIV 2024:arXiv:2406.03663v1. [PMID: 38883241 PMCID: PMC11177947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Précis A hybrid deep-learning model combines NFL reflectance and other OCT parameters to improve glaucoma diagnosis. Objective To investigate if a deep learning model could be used combine nerve fiber layer (NFL) reflectance and other OCT parameters for glaucoma diagnosis. Patients and Methods This is a prospective observational study where of 106 normal subjects and 164 perimetric glaucoma (PG) patients. Peripapillary NFL reflectance map, NFL thickness map, optic head analysis of disc, and macular ganglion cell complex thickness were obtained using spectral domain OCT. A hybrid deep learning model combined a fully connected network (FCN) and a convolution neural network (CNN) to develop to combine those OCT maps and parameters to distinguish normal and PG eyes. Two deep learning models were compared based on whether the NFL reflectance map was used as part of the input or not. Results The hybrid deep learning model with reflectance achieved 0.909 sensitivity at 99% specificity and 0.926 at 95%. The overall accuracy was 0.948 with 0.893 sensitivity and 1.000 specificity, and the AROC was 0.979, which is significantly better than the logistic regression models (p < 0.001). The second best model is the hybrid deep learning model w/o reflectance, which also had significantly higher AROC than logistic regression models (p < 0.001). Logistic regression with reflectance model had slightly higher AROC or sensitivity than the other logistic regression model without reflectance (p = 0.024). Conclusions Hybrid deep learning model significantly improved the diagnostic accuracy, without or without NFL reflectance. Hybrid deep learning model, combining reflectance/NFL thickness/GCC thickness/ONH parameter, may be a practical model for glaucoma screen purposes.
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Affiliation(s)
- Ou Tan
- Casey Eye Institute, Oregon Health & Science University
| | | | - Brian A Francis
- Doheny Eye Center and David Geffen School of Medicine at UCLA
| | | | | | - David Huang
- Casey Eye Institute, Oregon Health & Science University
| | - Dongseok Choi
- Casey Eye Institute, Oregon Health & Science University
- OHSU-PSU School of Public Health, University of Miami
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Swanson WH, King BJ, Alluwimi MS, Malik R. Predicting perimetric defects from en face maps of retinal nerve fibre layer reflectance. Ophthalmic Physiol Opt 2024; 44:613-625. [PMID: 38404167 PMCID: PMC10999345 DOI: 10.1111/opo.13289] [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: 09/19/2023] [Revised: 02/05/2024] [Accepted: 02/06/2024] [Indexed: 02/27/2024]
Abstract
PURPOSE To develop criteria to predict visual hemifields with deep perimetric defects based on retinal nerve fibre layer (RNFL) reflectance, in a transparent process whose components can be assessed by independent laboratories analysing data from their own small groups. METHODS The analysis was carried out in four stages, using three independent groups of patients-30, 33 and 62 participants-with glaucoma and age-similar controls. The first stage used Group 1 to develop a criterion for RNFL reflectance images at 24, 36 or 48 μm below the inner limiting membrane (ILM). The second stage evaluated the criterion using Group 2. The third stage developed a second criterion to improve performance for Groups 1 and 2 combined. The fourth stage evaluated the second criterion with Group 3. Confidence intervals for sensitivity and specificity were then computed by combining results from all three groups. RESULTS The first criterion identified all hemifields with deep defects and no hemifields from controls, using a within-eye reference for healthy RNFL. For Group 2, specificity remained high but sensitivity was reduced. The second criterion improved sensitivity by using location-specific reference values. For Group 3, sensitivity remained high but reduced specificity was found. Confidence intervals showed substantial overlap for the two criteria. CONCLUSIONS We developed two criteria to identify patients with deep perimetric defects with high specificity and sensitivity. Several improvements are warranted: automated identification of the fovea-disc angle and optic disc locations, evaluation of normal variation in patterns of RNFL thickness, improved segmentation of ILM and major vasculature, reduction of within-eye variability in RNFL reflectance of healthy eyes, assessment of effects of image quality, assessment of effects of comorbidity and effectiveness of other devices.
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Affiliation(s)
| | - Brett J King
- Indiana University School of Optometry, Bloomington, Indiana, USA
| | - Muhammed S Alluwimi
- Department of Optometry, College of Applied Medical Sciences, Qassim University, Buraidah, Saudi Arabia
| | - Rizwan Malik
- Department of Surgery, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
- King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia
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Alluwimi MS, Swanson WH, Malik R. Structure-function assessment in glaucoma based on perimetric sensitivity and en face optical coherence tomography images of retinal nerve fiber bundles. Sci Rep 2023; 13:2497. [PMID: 36781886 PMCID: PMC9925735 DOI: 10.1038/s41598-023-28917-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 01/27/2023] [Indexed: 02/15/2023] Open
Abstract
Many studies have assessed structure-function relations in glaucoma, but most without topographical comparison across the central 30°. We present a method for assessing structure-function relations with en face images of retinal nerve fiber layer (RNFL) bundles allowing topographical comparison across much of this retinal area. Forty-four patients with glaucoma (median age 61 years) were recruited and tested with Optical Coherence Tomography (OCT) and perimetry. Six rectangular volume scans were gathered, and then montaged to provide en face views of the RNFL bundles. We calculated the proportion of locations showing a perimetric defect that also showed an en face RNFL defect; and the proportion of locations falling on an RNFL defect that also showed a perimetric defect. A perimetric defect for a location was defined as a total deviation (TD) value equal to or deeper than -4 dB. We found that the median (IQR) number of locations with abnormal RNFL bundle reflectance that also had abnormal TD was 78% (60%) and for locations with abnormal TD that also had abnormal RNFL bundle reflectance was 75% (44%). We demonstrated a potential approach for structure-function assessment in glaucoma by presenting a topographic reflectance map, confirming results of previous studies and including larger retinal regions.
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Affiliation(s)
- Muhammed S Alluwimi
- Department of Optometry, College of Applied Medical Sciences, Qassim University, Qassim, Saudi Arabia.
| | | | - Rizwan Malik
- Glaucoma Division, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia
- Department of Surgery, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
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Cheloni R, Denniss J. Concordance of Objectively Detected Retinal Nerve Fiber Bundle Defects in En Face OCT Images with Conventional Structural and Functional Changes in Glaucoma. Ophthalmol Glaucoma 2023; 6:78-92. [PMID: 35835434 DOI: 10.1016/j.ogla.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/24/2022] [Accepted: 07/05/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE To assess how objectively detected defects in retinal nerve fiber bundle (RNFB) reflectance on en face OCT images relate to circumpapillary retinal nerve fiber layer thickness (cpRNFLT) and visual field defects. DESIGN Cross-sectional study. PARTICIPANTS Sixteen participants with early glaucoma and 29 age-matched healthy controls, of whom 22 had usable en face images for the establishment of normative levels of RNFB reflectance. METHODS All the participants underwent cpRNFLT scans, visual field examination, and wide-field OCT. En face reflectivity was assessed objectively using the Summary of Multiple Anatomically Adjusted Slabs method. En face defects were deemed concordant with cpRNFLT when they had at least 1 cpRNFLT point with P < 0.01, within ± 15° of the predicted insertion on the optic disc. Visual fields were examined using custom suprathreshold perimetry and SITA Standard 24-2. For each visual field location, the corresponding reflectance was deemed abnormal if any en face superpixel within ± 1° was abnormal. The overall, positive, and negative agreements were measured in each participant. MAIN OUTCOME MEASURES Proportion of concordant defects between en face reflectance analysis and cpRNFLT (%) as well as overall, positive, and negative agreements between en face reflectance analysis and visual field results. RESULTS Most en face abnormalities had concordant cpRNFLT defects in the mapped sector (median proportion concordant, 0.85; interquartile range, 0.74-0.95). In eyes with glaucoma, a median of 8.1% (range, 2.4%-23.7%) and 14.9% (range, 3.5%-29.1%) locations showed corresponding en face and visual field defects using 24-2 and custom perimetry, respectively. Both the perimetric strategies had moderate-to-good raw agreement with en face analysis (0.66-0.68), with stronger agreement on normal findings than on defects (0.77-0.78 and 0.4-0.44). CONCLUSIONS Objectively extracted reflectance defects showed strong concordance with conventional cpRNFLT damage and good agreement with perimetry, which could be enhanced by further minimization of image artifacts.
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Affiliation(s)
- Riccardo Cheloni
- School of Optometry and Vision Science, University of Bradford, United Kingdom
| | - Jonathan Denniss
- School of Optometry and Vision Science, University of Bradford, United Kingdom.
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Adaptive IoU Thresholding for Improving Small Object Detection: A Proof-of-Concept Study of Hand Erosions Classification of Patients with Rheumatic Arthritis on X-ray Images. Diagnostics (Basel) 2022; 13:diagnostics13010104. [PMID: 36611395 PMCID: PMC9818241 DOI: 10.3390/diagnostics13010104] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/08/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
Abstract
In recent years, much research evaluating the radiographic destruction of finger joints in patients with rheumatoid arthritis (RA) using deep learning models was conducted. Unfortunately, most previous models were not clinically applicable due to the small object regions as well as the close spatial relationship. In recent years, a new network structure called RetinaNets, in combination with the focal loss function, proved reliable for detecting even small objects. Therefore, the study aimed to increase the recognition performance to a clinically valuable level by proposing an innovative approach with adaptive changes in intersection over union (IoU) values during training of Retina Networks using the focal loss error function. To this end, the erosion score was determined using the Sharp van der Heijde (SvH) metric on 300 conventional radiographs from 119 patients with RA. Subsequently, a standard RetinaNet with different IoU values as well as adaptively modified IoU values were trained and compared in terms of accuracy, mean average accuracy (mAP), and IoU. With the proposed approach of adaptive IoU values during training, erosion detection accuracy could be improved to 94% and an mAP of 0.81 ± 0.18. In contrast Retina networks with static IoU values achieved only an accuracy of 80% and an mAP of 0.43 ± 0.24. Thus, adaptive adjustment of IoU values during training is a simple and effective method to increase the recognition accuracy of small objects such as finger and wrist joints.
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Cheung H, Swanson WH, King BJ. Within-eye and between-subject variability for reflectance of the retinal nerve fibre layer. Ophthalmic Physiol Opt 2022; 42:1316-1325. [PMID: 35915921 PMCID: PMC9547838 DOI: 10.1111/opo.13027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 01/05/2023]
Abstract
PURPOSE Reflectance of retinal nerve fibre layer (RNFL) can contribute to detecting the presence of glaucomatous damage and defining its extent. As a step towards developing a normative database for RNFL reflectance, we assessed within-eye and between-subject variability for RNFL reflectance in healthy eyes. METHODS Vertical 30° × 15° volume scans at the optic disc were gathered using SD-OCT (Spectralis OCT) from people free of eye disease. Scans were gathered for both eyes of 30 younger adults (mean ± SD = 27 ± 3 years) and for one eye of 30 older adults (68 ± 8 years). Reflectance was quantified for each voxel as the depth-resolved attenuation coefficient (AC). Values for AC were extracted for four slabs (0-52, 24-52, 24-36 and 36-60 μm) and at depths from 24 to 60 μm below the inner limiting membrane (ILM) in 4 μm steps. RESULTS Between-subject and within-eye standard deviations (SDs) for the logarithm of AC were similar; median differences were 0.02-0.03 log unit across all four slabs and depths from 24 to 48 μm. Means for the logarithm of AC were higher for younger than older eyes by ~0.1 log unit; this age effect was not due to differences in the raw reflectance of the RNFL, but rather to age-related changes in reflectance of deeper retina affecting the calculation of AC. CONCLUSIONS In both groups, within-eye variability in RNFL reflectance near the optic disc was similar to between-subject variability. A better understanding of within-eye variability would be useful for developing normative databases.
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Affiliation(s)
- Hin Cheung
- Indiana University School of OptometryBloomingtonIndianaUSA
| | | | - Brett J. King
- Indiana University School of OptometryBloomingtonIndianaUSA
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Camara J, Neto A, Pires IM, Villasana MV, Zdravevski E, Cunha A. Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. J Imaging 2022; 8:19. [PMID: 35200722 PMCID: PMC8878383 DOI: 10.3390/jimaging8020019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease's progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.
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Affiliation(s)
- José Camara
- R. Escola Politécnica, Universidade Aberta, 1250-100 Lisboa, Portugal;
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal;
| | - Alexandre Neto
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal;
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal;
| | - Ivan Miguel Pires
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal;
- Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
| | - María Vanessa Villasana
- Centro Hospitalar Universitário Cova da Beira, 6200-251 Covilhã, Portugal;
- UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia;
| | - António Cunha
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal;
- Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal;
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