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Jin Z, Chen X, Jiang C, Feng X, Zou D, Lu Y, Li J, Ren Q, Zhou C. Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening. Br J Ophthalmol 2024:bjo-2023-323283. [PMID: 38697799 DOI: 10.1136/bjo-2023-323283] [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: 01/28/2023] [Accepted: 04/18/2024] [Indexed: 05/05/2024]
Abstract
BACKGROUND/AIMS To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks. METHODS Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment. RESULTS This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC548) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%). CONCLUSIONS This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.
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Affiliation(s)
- Zi Jin
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - Xuhui Chen
- Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Chunxia Jiang
- Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Ximeng Feng
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Da Zou
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Yanye Lu
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Jinying Li
- Department of Ophthalmology, Peking University Shenzhen Hospital, Shenzhen, China
| | - Qiushi Ren
- Department of Biomedical Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
- Department of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
- Department of Biomedical Engineering, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Centre, Beijing, China
| | - Chuanqing Zhou
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China
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Lee SJ, Yang KM, Lee KB, Park NC. Design of illumination system using characterized illuminances for smartphone-based fundus camera. OPTICS AND LASERS IN ENGINEERING 2023; 168:107664. [PMID: 37251556 PMCID: PMC10204717 DOI: 10.1016/j.optlaseng.2023.107664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 04/12/2023] [Accepted: 05/12/2023] [Indexed: 05/31/2023]
Abstract
Recently, smartphone-based fundus camera (SBFC) research has been actively conducted in response to the need to expand medical infrastructure in underdeveloped countries and the increased telemedicine since the COVID-19 pandemic. Compared to the conventional table-top system, SBFCs have technical challenges that make it difficult to guarantee uniform illumination and avoid back-reflection because of the design constraints of minimizing the form factor and cost. This paper proposes a novel illumination design methodology using characterized illuminance to obtain high-quality fundus images for SBFCs. Key performance indicators (KPIs), such as retinal uniformity, back-reflection suppression, and optical efficiency, were defined to evaluate the performance of the illumination system. Each KPI was calculated using optical simulation software based on Monte-Carlo ray tracing and mapped into a normalized three-dimensional coordinate, the retinal illumination performance space (RIPS). In RIPS, the KPIs are combined into a single parameter ΔRIPS, which is the quantitative difference evaluated as the Euclidean distance between the ideal and actual design point. A compact SBFC illumination system with five design variables was presented to verify the proposed methodology. The final design values at the minimum ΔRIPS were determined using the Taguchi method and response surface methodology. Finally, a working prototype was built, and fundus images were acquired by clinical testing under institutional review board approval. The fundus image had sufficient brightness and resolution to diagnose the lesion with a viewing angle of approximately 50° in one shot.
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Affiliation(s)
- Seung-Jae Lee
- Department of Mechanical Engineering, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul 03722, South Korea
- Samsung Electronics Co., Ltd., 129 Samsung-ro, Yeongton-gu, Suwon-si, Gyeonggi-do 16677, South Korea
| | - Kyung-Mo Yang
- Samsung Electronics Co., Ltd., 129 Samsung-ro, Yeongton-gu, Suwon-si, Gyeonggi-do 16677, South Korea
| | - Keun-Bum Lee
- Samsung Electronics Co., Ltd., 129 Samsung-ro, Yeongton-gu, Suwon-si, Gyeonggi-do 16677, South Korea
| | - No-Cheol Park
- Department of Mechanical Engineering, Yonsei University, 50 Yonsei-ro Seodaemun-gu, Seoul 03722, South Korea
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Labkovich M, Paul M, Kim E, A. Serafini R, Lakhtakia S, Valliani AA, Warburton AJ, Patel A, Zhou D, Sklar B, Chelnis J, Elahi E. Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review. Digit Health 2022; 8:20552076221090042. [PMID: 35558637 PMCID: PMC9087242 DOI: 10.1177/20552076221090042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 03/09/2022] [Indexed: 11/19/2022] Open
Abstract
Vision impairment continues to be a major global problem, as the WHO estimates 2.2 billion people struggling with vision loss or blindness. One billion of these cases, however, can be prevented by expanding diagnostic capabilities. Direct global healthcare costs associated with these conditions totaled $255 billion in 2010, with a rapid upward projection to $294 billion in 2020. Accordingly, WHO proposed 2030 targets to enhance integration and patient-centered vision care by expanding refractive error and cataract worldwide coverage. Due to the limitations in cost and portability of adapted vision screening models, there is a clear need for new, more accessible vision testing tools in vision care. This comparative, systematic review highlights the need for new ophthalmic equipment and approaches while looking at existing and emerging technologies that could expand the capacity for disease identification and access to diagnostic tools. Specifically, the review focuses on portable hardware- and software-centered strategies that can be deployed in remote locations for detection of ophthalmic conditions and refractive error. Advancements in portable hardware, automated software screening tools, and big data-centric analytics, including machine learning, may provide an avenue for improving ophthalmic healthcare.
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Affiliation(s)
- Margarita Labkovich
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Megan Paul
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Eliott Kim
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Randal A. Serafini
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
- Nash Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | | | - Aly A Valliani
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Andrew J Warburton
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Aashay Patel
- Department of Medical Education, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Davis Zhou
- Department of Ophthalmology, New York Eye and Ear Infirmary of Mount
Sinai, New York, NY, USA
| | - Bonnie Sklar
- Department of Ophthalmology, Wills Eye Hospital, Philadelphia, PA, USA
| | - James Chelnis
- Department of Ophthalmology, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Ebrahim Elahi
- Department of Ophthalmology, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
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Schramm S, Dietzel A, Link D, Blum MC, Klee S. 3D retinal imaging and measurement using light field technology. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-210183RR. [PMID: 34921542 PMCID: PMC8678491 DOI: 10.1117/1.jbo.26.12.126002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
SIGNIFICANCE Light-field fundus photography has the potential to be a new milestone in ophthalmology. Up-to-date publications show only unsatisfactory image quality, preventing the use of depth measurements. We show that good image quality and, consequently, reliable depth measurements are possible, and we investigate the current challenges of this novel technology. AIM We investigated whether light field (LF) imaging of the retina provides depth information, on which structures the depth is estimated, which illumination wavelength should be used, whether deeper layers are measurable, and what kinds of artifacts occur. APPROACH The technical setup, a mydriatic fundus camera with an LF imager, and depth estimation were validated by an eye model and in vivo measurements of three healthy subjects and three subjects with suspected glaucoma. Comparisons between subjects and the corresponding optical coherence tomography (OCT) measurements were used for verification of the depth estimation. RESULTS This LF setup allowed for three-dimensional one-shot imaging and depth estimation of the optic disc with green light. In addition, a linear relationship was found between the depth estimates of the OCT and those of the setup developed here. This result is supported by the eye model study. Deeper layers were not measurable. CONCLUSIONS If image artifacts can be handled, LF technology has the potential to help diagnose and monitor glaucoma risk at an early stage through a rapid, cost-effective one-shot technology.
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Affiliation(s)
- Stefan Schramm
- Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Faculty of Computer Sciences and Automation, Ilmenau, Germany
| | - Alexander Dietzel
- Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Faculty of Computer Sciences and Automation, Ilmenau, Germany
| | - Dietmar Link
- Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Faculty of Computer Sciences and Automation, Ilmenau, Germany
| | - Maren-Christina Blum
- Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Faculty of Computer Sciences and Automation, Ilmenau, Germany
| | - Sascha Klee
- Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Faculty of Computer Sciences and Automation, Ilmenau, Germany
- Karl Landsteiner University of Health Sciences, Division Biostatistics and Data Science, Department of General Health Studies, Krems, Austria
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Hahne C, Aggoun A. PlenoptiCam v1.0: A Light-Field Imaging Framework. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:6757-6771. [PMID: 34280098 DOI: 10.1109/tip.2021.3095671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Light-field cameras play a vital role for rich 3D information retrieval in narrow range depth sensing applications. The key obstacle in composing light-fields from exposures taken by a plenoptic camera is to computationally calibrate, align and rearrange four-dimensional image data. Several attempts have been proposed to enhance the overall image quality by tailoring pipelines dedicated to particular plenoptic cameras and improving the consistency across viewpoints at the expense of high computational loads. The framework presented herein advances prior outcomes thanks to its novel micro image scale-space analysis for generic camera calibration independent of the lens specifications and its parallax-invariant, cost-effective viewpoint color equalization from optimal transport theory. Artifacts from the sensor and micro lens grid are compensated in an innovative way to enable superior quality in sub-aperture image extraction, computational refocusing and Scheimpflug rendering with sub-sampling capabilities. Benchmark comparisons using established image metrics suggest that our proposed pipeline outperforms state-of-the-art tool chains in the majority of cases. Results from a Wasserstein distance further show that our color transfer outdoes the existing transport methods. Our algorithms are released under an open-source license, offer cross-platform compatibility with few dependencies and different user interfaces. This makes the reproduction of results and experimentation with plenoptic camera technology convenient for peer researchers, developers, photographers, data scientists and others working in this field.
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Hunt B, Ruiz AJ, Pogue BW. Smartphone-based imaging systems for medical applications: a critical review. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:JBO-200421VR. [PMID: 33860648 PMCID: PMC8047775 DOI: 10.1117/1.jbo.26.4.040902] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 03/29/2021] [Indexed: 05/15/2023]
Abstract
SIGNIFICANCE Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed. AIM Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances. APPROACH First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed. RESULTS The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo, multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications. CONCLUSIONS A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forward.
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Affiliation(s)
- Brady Hunt
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States
- Address all correspondence to Brady Hunt,
| | - Alberto J. Ruiz
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States
| | - Brian W. Pogue
- Dartmouth College, Thayer School of Engineering, Hanover, New Hampshire, United States
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Chen Y, Jin X, Xiong B. Optical-aberrations-corrected light field re-projection for high-quality plenoptic imaging. OPTICS EXPRESS 2020; 28:3057-3072. [PMID: 32121981 DOI: 10.1364/oe.381720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 01/11/2020] [Indexed: 06/10/2023]
Abstract
The singlet plenoptic camera, which consists of a single lens, microlens array (MLA) and image sensor, possesses the superiority that the imaging system is compact and lightweight, which is beneficial to miniaturization. However, such plenoptic cameras suffer from severe optical aberrations and their imaging quality is inferior for post-capture processing. Therefore, this paper proposes an optical-aberrations-corrected light field re-projection method to obtain high-quality singlet plenoptic imaging. First, optical aberrations are modeled by Seidel polynomials and included into point spread function (PSF) modeling. The modeled PSF is subsequently used to reconstruct imaging object information. Finally, the reconstructed imaging object information is re-projected back to the plenoptic imaging plane to obtain high-quality plenoptic images without optical aberrations. PSF modeling is validated by a self-built singlet plenoptic camera and the utility of the proposed optical-aberrations-corrected light field re-projection method is verified by numerical simulations and real imaging experiments.
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Trucco E, McNeil A, McGrory S, Ballerini L, Mookiah MRK, Hogg S, Doney A, MacGillivray T. Validation. COMPUTATIONAL RETINAL IMAGE ANALYSIS 2019:157-170. [DOI: 10.1016/b978-0-08-102816-2.00009-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
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Teikari P, Najjar RP, Schmetterer L, Milea D. Embedded deep learning in ophthalmology: making ophthalmic imaging smarter. Ther Adv Ophthalmol 2019; 11:2515841419827172. [PMID: 30911733 PMCID: PMC6425531 DOI: 10.1177/2515841419827172] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 12/20/2018] [Indexed: 01/22/2023] Open
Abstract
Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for 'active acquisition'-embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning-based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via 'active acquisition' serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning-based clinical data mining.
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Affiliation(s)
- Petteri Teikari
- Visual Neurosciences Group, Singapore Eye Research Institute, Singapore
- Advanced Ocular Imaging, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Raymond P. Najjar
- Visual Neurosciences Group, Singapore Eye Research Institute, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Leopold Schmetterer
- Visual Neurosciences Group, Singapore Eye Research Institute, Singapore
- Advanced Ocular Imaging, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
- Christian Doppler Laboratory for Ocular and Dermal Effects of Thiomers, Medical University of Vienna, Vienna, Austria
| | - Dan Milea
- Visual Neurosciences Group, Singapore Eye Research Institute, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, National University of Singapore, Singapore
- Neuro-Ophthalmology Department, Singapore National Eye Centre, Singapore
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