NEXT-GENERATION BIOMEDICAL IMAGING AND DIAGNOSTICS USING PHOTONICS AND ARTIFICIAL INTELLIGENCE

Authors

  • Amadi Oko Amadi Department of computer Engineering Technology Akanu Ibiam Federal Polytechnic Unwana-Ebonyi State
  • Jidere Ann Obiageli Department of Computer Engineering Technology Federal Polytechnic Nekede,  Imo state
  • Ojikeya Chinerezi Sergiuspaulus Department of Computer Engineering Technology Federal Polytechnic Nekede,  Imo state
  • Okpo Charles Nnanna Department of Electrical /Electronic Engineering Technology Akanu Ibiam Federal Polytechnic Unwana-Ebonyi state
  • Oruruo Ogechukwu Nsude Department of computer Engineering Technology Akanu Ibiam Federal Polytechnic Unwana-Ebonyi State.
  • Akujobi Leonard Chibuzo Department of Computer Engineering Technology Federal Polytechnic Nekede, Imo state

Keywords:

photonics, AI--photoncs, Optical coherence tomography, photoacoustic imaging

Abstract

The convergence of photonics and artificial intelligence (AI) is redefining the landscape of biomedical imaging and diagnostics by significantly enhancing imaging resolution, acquisition speed, and diagnostic precision. Photonic technologies such as Optical Coherence Tomography (OCT), Stimulated Raman Histology (SRH), and Photoacoustic Imaging (PAI) offer non-invasive, high-resolution visualization of anatomical and functional tissue characteristics. When integrated with AI—particularly deep learning algorithms—these modalities gain advanced capabilities for real-time image analysis, anomaly detection, and predictive diagnostics. Empirical evidence supports the impact of this integration: for example, Orringer et al. (2019) demonstrated that coupling SRH with a convolutional neural network (CNN) enabled intraoperative brain tumor diagnosis with 94.6% accuracy in under three minutescompared to traditional frozen-section pathology requiring 20–30 minutes. Similarly, Liu et al. (2020) applied a deep learning model to OCT images and achieved diagnostic performance for diabetic retinopathy on par with expert ophthalmologists, with an area under the ROC curve (AUC) of 0.991. In another study, Lan et al. (2022) developed an AI-assisted photoacoustic imaging framework to classify breast cancer subtypes, achieving 92% accuracy across multiple imaging depths and conditions. These findings substantiate the transformative potential of AI-photonics systems in delivering rapid, reproducible, and scalable diagnostic solutions. This paper critically reviews these empirical advances, presents case-specific applications, and proposes a framework for the clinical translation of integrated AI-photonics technologies.

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Published

2025-06-07 — Updated on 2025-06-07

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Vol. 2 No. 3 (2025): Advanced Journal of Engineering and Scientific Applications (AJESA) Volume 2 No. 3 May/June 2025

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