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Bioengineering
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AI in OCT (Optical Coherence Tomography) Image Analysis
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AI in OCT (Optical Coherence Tomography) Image Analysis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 9766

Special Issue Editors

Dr. Juhwan Lee

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Guest Editor
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
Interests: medical image processing; machine/deep learning; intravascular optical coherence tomography; computed tomography
Dr. Kiran Kumar Vupparaboina

E-Mail Website
Guest Editor
Department of Ophthalmology, UPMC Vision Institute, University of Pittsburgh, UPMC Mercy Pavilion, 1622 Locust Street, Pittsburgh, PA 15219, USA
Interests: medical image analysis; computer vision; ophthalmology; artificial intelligence, machine learning

Special Issue Information

Dear Colleagues,

Optical coherence tomography (OCT) is a non-invasive diagnostic technique that utilizes near-infrared light. With its exceptional high-contrast and high-resolution capabilities, OCT has undeniably emerged as a promising imaging tool in various clinical diagnostic domains. Notably, this versatile technology finds applications in several clinical areas, which include:

  • Retinal Imaging: Including the detection of diabetic macular edema, epithelial mapping for refractive surgery, diagnosis and management of patients with neuro-ophthalmic conditions, and imaging of retinal microcirculation.
  • Dental Imaging: Involving the detection of age-associated changes in teeth, diagnosis of dental caries, and tooth wear.
  • Intravascular Imaging: Covering the imaging of coronary atherosclerotic plaque, stent strut analysis, fractional flow reserve measurement, and assessment of outcomes in percutaneous coronary interventions.

Recent advances in artificial intelligence (AI) have catalyzed further innovative applications of OCT in diagnosis, treatment planning, and the monitoring of treatment outcomes. A broader perspective is often the key to unlocking the full potential of innovation. As AI methodologies and their applications continue to evolve, OCT is progressively venturing into new frontiers of clinical utility.

We are pleased to announce our forthcoming Special Issue, which focuses on the latest findings related to the applications of AI in conjunction with OCT. Topics of interest are comprehensive, but are not limited to:

  • Ex-vivo tissue imaging using OCT;
  • OCT for retinal imaging;
  • OCT for dental imaging;
  • OCT for intravascular imaging;
  • AI applications to improve the OCT image quality;
  • Novel image analysis for improving the OCT image quality.

Dr. Juhwan Lee
Dr. Kiran Kumar Vupparaboina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • retinal imaging
  • dental imaging
  • intravascular imaging
  • optical coherence tomography
  • machine learning
  • deep learning
  • advanced image processing

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Published Papers (6 papers)

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Research

22 pages, 5835 KiB  
Article
Multimodal Classification of Alzheimer’s Disease Using Longitudinal Data Analysis and Hypergraph Regularized Multi-Task Feature Selection
by Shuaiqun Wang, Huan Zhang and Wei Kong
Bioengineering 2025, 12(4), 388; https://doi.org/10.3390/bioengineering12040388 - 5 Apr 2025
Viewed by 284
Abstract
Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the [...] Read more.
Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the inherent longitudinal nature of neuroimaging applications. Therefore, in this paper, we propose a multi-task feature selection algorithm for Alzheimer’s disease classification based on longitudinal imaging and hypergraphs (THM2TFS). Our methodology establishes a multi-task learning framework where feature selection at each temporal interval is treated as an individual task within each imaging modality. To address temporal dependencies, we implement group sparse regularization with two critical components: (1) a hypergraph-induced regularization term that captures high-order structural relationships among subjects through hypergraph Laplacian modeling, and (2) a fused sparse Laplacian regularization term that encodes progressive pathological changes in brain regions across time points. The selected features are subsequently integrated via multi-kernel support vector machines for final classification. We used functional magnetic resonance imaging and structural functional magnetic resonance imaging data from Alzheimer’s Disease Neuroimaging Initiative at four different time points (baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4)) to evaluate our method. The experimental results show that the accuracy rates of 96.75%, 93.45, and 83.78 for the three groups of classification tasks (AD vs. NC, MCI vs. NC and AD vs. MCI) are obtained, respectively, which indicates that the proposed method can not only capture the relevant information between longitudinal image data well, but also the classification accuracy of Alzheimer’s disease is improved, and it helps to identify the biomarkers associated with Alzheimer’s disease. Full article
(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)
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17 pages, 8265 KiB  
Article
Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation
by Peter Racioppo, Aya Alhasany, Nhuan Vu Pham, Ziyuan Wang, Giulia Corradetti, Gary Mikaelian, Yannis M. Paulus, SriniVas R. Sadda and Zhihong Hu
Bioengineering 2025, 12(4), 334; https://doi.org/10.3390/bioengineering12040334 - 23 Mar 2025
Viewed by 505
Abstract
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique used to visualize retinal blood flow and identify changes in vascular density and enlargement or distortion of the foveal avascular zone (FAZ), which are indicators of various eye diseases. Although several automated FAZ [...] Read more.
Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique used to visualize retinal blood flow and identify changes in vascular density and enlargement or distortion of the foveal avascular zone (FAZ), which are indicators of various eye diseases. Although several automated FAZ detection and segmentation algorithms have been developed for use with OCTA, their performance can vary significantly due to differences in data accessibility of OCTA in different retinal pathologies, and differences in image quality in different subjects and/or different OCTA devices. For example, data from subjects with direct macular damage, such as in age-related macular degeneration (AMD), are more readily available in eye clinics, while data on macular damage due to systemic diseases like Alzheimer’s disease are often less accessible; data from healthy subjects may have better OCTA quality than subjects with ophthalmic pathologies. Typically, segmentation algorithms make use of convolutional neural networks and, more recently, vision transformers, which make use of both long-range context and fine-grained detail. However, transformers are known to be data-hungry, and may overfit small datasets, such as those common for FAZ segmentation in OCTA, to which there is limited access in clinical practice. To improve model generalization in low-data or imbalanced settings, we propose a multi-condition transformer-based architecture that uses four teacher encoders to distill knowledge into a shared base model, enabling the transfer of learned features across multiple datasets. These include intra-modality distillation using OCTA datasets from four ocular conditions: healthy aging eyes, Alzheimer’s disease, AMD, and diabetic retinopathy; and inter-modality distillation incorporating color fundus photographs of subjects undergoing laser photocoagulation therapy. Our multi-condition model achieved a mean Dice Index of 83.8% with pretraining, outperforming single-condition models (mean of 83.1%) across all conditions. Pretraining on color fundus photocoagulation images improved the average Dice Index by a small margin on all conditions except AMD (1.1% on single-condition models, and 0.1% on multi-condition models). Our architecture demonstrates potential for broader applications in detecting and analyzing ophthalmic and systemic diseases across diverse imaging datasets and settings. Full article
(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)
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13 pages, 3041 KiB  
Article
Detection of Disease Features on Retinal OCT Scans Using RETFound
by Katherine Du, Atharv Ramesh Nair, Stavan Shah, Adarsh Gadari, Sharat Chandra Vupparaboina, Sandeep Chandra Bollepalli, Shan Sutharahan, José-Alain Sahel, Soumya Jana, Jay Chhablani and Kiran Kumar Vupparaboina
Bioengineering 2024, 11(12), 1186; https://doi.org/10.3390/bioengineering11121186 - 25 Nov 2024
Viewed by 1602
Abstract
Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling [...] Read more.
Eye diseases such as age-related macular degeneration (AMD) are major causes of irreversible vision loss. Early and accurate detection of these diseases is essential for effective management. Optical coherence tomography (OCT) imaging provides clinicians with in vivo, cross-sectional views of the retina, enabling the identification of key pathological features. However, manual interpretation of OCT scans is labor-intensive and prone to variability, often leading to diagnostic inconsistencies. To address this, we leveraged the RETFound model, a foundation model pretrained on 1.6 million unlabeled retinal OCT images, to automate the classification of key disease signatures on OCT. We finetuned RETFound and compared its performance with the widely used ResNet-50 model, using single-task and multitask modes. The dataset included 1770 labeled B-scans with various disease features, including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, and pigment epithelial detachment (PED). The performance was evaluated using accuracy and AUC-ROC values, which ranged across models from 0.75 to 0.77 and 0.75 to 0.80, respectively. RETFound models display comparable specificity and sensitivity to ResNet-50 models overall, making it also a promising tool for retinal disease diagnosis. These findings suggest that RETFound may offer improved diagnostic accuracy and interpretability for specific tasks, potentially aiding clinicians in more efficient and reliable OCT image analysis. Full article
(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)
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12 pages, 2648 KiB  
Article
The Use of Artificial Intelligence for Estimating Anterior Chamber Depth from Slit-Lamp Images Developed Using Anterior-Segment Optical Coherence Tomography
by Eisuke Shimizu, Kenta Tanaka, Hiroki Nishimura, Naomichi Agata, Makoto Tanji, Shintato Nakayama, Rohan Jeetendra Khemlani, Ryota Yokoiwa, Shinri Sato, Daisuke Shiba and Yasunori Sato
Bioengineering 2024, 11(10), 1005; https://doi.org/10.3390/bioengineering11101005 - 9 Oct 2024
Cited by 2 | Viewed by 1953
Abstract
Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. [...] Read more.
Primary angle closure glaucoma (PACG) is a major cause of visual impairment, particularly in Asia. Although effective screening tools are necessary, the current gold standard is complex and time-consuming, requiring extensive expertise. Artificial intelligence has introduced new opportunities for innovation in ophthalmic imaging. Anterior chamber depth (ACD) is a key risk factor for angle closure and has been suggested as a quick screening parameter for PACG. This study aims to develop an AI algorithm to quantitatively predict ACD from anterior segment photographs captured using a portable smartphone slit-lamp microscope. We retrospectively collected 204,639 frames from 1586 eyes, with ACD values obtained by anterior-segment OCT. We developed two models, (Model 1) diagnosable frame extraction and (Model 2) ACD estimation, using SWSL ResNet as the machine learning model. Model 1 achieved an accuracy of 0.994. Model 2 achieved an MAE of 0.093 ± 0.082 mm, an MSE of 0.123 ± 0.170 mm, and a correlation of R = 0.953. Furthermore, our model’s estimation of the risk for angle closure showed a sensitivity of 0.943, specificity of 0.902, and an area under the curve (AUC) of 0.923 (95%CI: 0.878–0.968). We successfully developed a high-performance ACD estimation model, laying the groundwork for predicting other quantitative measurements relevant to PACG screening. Full article
(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)
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12 pages, 5572 KiB  
Article
Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmentation of Retinal Biomarkers
by Mikhail Kulyabin, Aleksei Zhdanov, Andrey Pershin, Gleb Sokolov, Anastasia Nikiforova, Mikhail Ronkin, Vasilii Borisov and Andreas Maier
Bioengineering 2024, 11(9), 940; https://doi.org/10.3390/bioengineering11090940 - 19 Sep 2024
Cited by 1 | Viewed by 2758
Abstract
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in the early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema [...] Read more.
Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in the early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME), which affect millions of people globally. Over the past decade, the area of application of artificial intelligence (AI), particularly deep learning (DL), has significantly increased. The number of medical applications is also rising, with solutions from other domains being increasingly applied to OCT. The segmentation of biomarkers is an essential problem that can enhance the quality of retinal disease diagnostics. For 3D OCT scans, AI is beneficial since manual segmentation is very labor-intensive. In this paper, we employ the new SAM 2 and MedSAM 2 for the segmentation of OCT volumes for two open-source datasets, comparing their performance with the traditional U-Net. The model achieved an overall Dice score of 0.913 and 0.902 for macular holes (MH) and intraretinal cysts (IRC) on OIMHS and 0.888 and 0.909 for intraretinal fluid (IRF) and pigment epithelial detachment (PED) on the AROI dataset, respectively. Full article
(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)
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17 pages, 7102 KiB  
Article
Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death
by Juhwan Lee, Yazan Gharaibeh, Vladislav N. Zimin, Justin N. Kim, Neda S. Hassani, Luis A. P. Dallan, Gabriel T. R. Pereira, Mohamed H. E. Makhlouf, Ammar Hoori and David L. Wilson
Bioengineering 2024, 11(8), 843; https://doi.org/10.3390/bioengineering11080843 - 19 Aug 2024
Viewed by 1606
Abstract
This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed [...] Read more.
This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98–5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800–0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches. Full article
(This article belongs to the Special Issue AI in OCT (Optical Coherence Tomography) Image Analysis)
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