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Diagnostics
Special Issues
Expanding Horizons: Optical Coherence Tomography in Cross-Specialty Diagnostic Practice
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Expanding Horizons: Optical Coherence Tomography in Cross-Specialty Diagnostic Practice

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Biomedical Optics".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 672

Special Issue Editor

Dr. Nicusor Iftimia

E-Mail Website
Guest Editor
Physical Sciences Inc., Andover, MA, USA
Interests: cancer diagnosis; cancer therapy; multimodal optical imaging; optical spectroscopy; biosensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue aims to collect works representing the state-of-the-art in OCT development and its application beyond ophthalmology, where it has traditionally been dominant.

Key aspects covered in the Special Issue include the following:

  1. AI-assisted OCT applications for improving clinical diagnosis;
  2. Functional OCT methods, such as angiography, Doppler OCT, and optical coherence elastography-focused on clinical diagnosis;
  3. New advances in the OCT technology, such as adaptive optics OCT (AO-OCT) for ophthalmology use;
  4. Multimodal imaging incorporating OCT with other techniques.

This issue seeks papers that will focus on clinical and preclinical applications in various fields like ophthalmology, dermatology, cardiology, neurology, dentistry, etc.

Dr. Nicusor Iftimia
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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

  • optical coherence tomography
  • cross-specialty diagnosis
  • AI-assisted OCT
  • OCT angiography
  • multimodal imaging

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Published Papers (1 paper)

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Research

11 pages, 5084 KB  
Article
AI-Assisted OCT Imaging for Core Needle Biopsy Guidance: The 1st in Humans Study
by Nicusor Iftimia, Poonam Yadav, Michael Primrose, Gopi Maguluri, Jack Jones, John Grimble and Rahul Anil Sheth
Diagnostics 2026, 16(5), 811; https://doi.org/10.3390/diagnostics16050811 - 9 Mar 2026
Viewed by 449
Abstract
Background: The heterogeneous nature of cancer with varying degrees of fat, necrosis, fibrosis, and varying degrees of tissue repair severely impacts the success of acquiring adequate tissue samples during percutaneous image-guided biopsy. Although ultrasound or CT fluoroscopy are used to identify tumor [...] Read more.
Background: The heterogeneous nature of cancer with varying degrees of fat, necrosis, fibrosis, and varying degrees of tissue repair severely impacts the success of acquiring adequate tissue samples during percutaneous image-guided biopsy. Although ultrasound or CT fluoroscopy are used to identify tumor location and thus to guide biopsy needle insertion, these technologies do not provide the necessary resolution to determine tissue composition and enable the selection of the most appropriate location for biopsy specimen extraction. As a result, biopsy must be repeated, leading to significant cost to the health care system. Methods: In this study, we introduce a combined optical imaging/artificial intelligence (OI/AI) methodology for the real-time assessment of tissue morphology at the tip of the biopsy needle, prior to the collection of a biopsy specimen. Addressing a significant clinical challenge, this approach aims to reduce the proportion of biopsy cores—currently as high as 40%—that yield low diagnostic value due to elevated adipose or low tumor content. Our methodology employs micron-scale optical coherence tomography (OCT) imaging to obtain detailed structural tissue information using a minimally invasive needle probe. The OCT images are automatically analyzed using a convolutional neural network (CNN)-driven AI software developed by our team. A U-net style architecture was used to segment regions of tumor from the OCT scans. U-Net is a specialized convolutional neural network (CNN) architecture designed for fast, precise image segmentation, which involves classifying each pixel in an image to outline objects. This streamlined approach shows promise to provide clinicians with real-time results, supporting more accurate and informed decisions regarding biopsy site selection. To evaluate this technology, we conducted a clinical study using a custom-made OCT imager and recorded OCT images from patients diagnosed with liver cancers. Expert OCT interpreters supplied annotated reference images that were used to train a custom AI algorithm. Results: OCT imaging with ~10 mm axial and 20 mm lateral resolution enabled the collection of high-quality images of the tissue. The AI analysis was performed offline. UNet achieved an AUC of ~0.877 on the validation dataset, indicating promising performance for the relatively small data set used to train the model. The AI model matched human interpretations approximately 90% of the time, highlighting its promise for making biopsy procedures both more accurate and more efficient. Conclusions: A novel OCT instrument and AI software were evaluated for assessing tissue composition at the tip of biopsy needle. The OCT instrument produced micron-scale resolution images of the tissue, enabling AI analysis and accurate real-time discrimination of tissue type. This preliminary study demonstrated the clinical potential of this technology for improving biopsy success. Full article
(This article belongs to the Special Issue Expanding Horizons: Optical Coherence Tomography in Cross-Specialty Diagnostic Practice)
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