Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications
Abstract
Significance: Hyperspectral imaging (HSI) is an advanced spectral imaging technique that captures spatial and spectral information across numerous wavelength bands. This capability allows tissue characterization, disease detection and diagnosis, surgical guidance, and digital histopathology, making it an increasingly valuable tool with wide biological and medical applications.
Aim: We aim to provide readers with (1) an understanding of the principles and technological advancements in HSI, (2) a comprehensive overview of HSI data processing and analysis methods, and (3) an updated survey of biomedical applications, from disease detection, intraoperative imaging, to histopathology.
Approach: A systematic literature search was conducted using PubMed and Google Scholar with the keyword "hyperspectral imaging." We previously published a comprehensive review paper on medical HSI in 2014, which was widely cited in the field. Therefore, this updated review focused on new technology advancements and emerging applications. Based on their biological and medical relevance, 612 HSI papers were included and analyzed in this review.
Results: Recent advances in HSI span both hardware and computational techniques, including improvements in sensor technology, data processing and analysis, short-wave near-infrared imaging, and deep-learning and AI tools. HSI is actively explored for various applications in oncology, neurology, ophthalmology, dermatology, cardiology, gastroenterology,…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.