ReviewAPL bioengineering2024
Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review.
Review in APL bioengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Emerging non-invasive tools for hair follicle assessment: a narrative review on hyperspectral and terahertz imaging.Lasers in medical science · 2026Review
- Automated Individual-Level ROI-to-Spectrum Extraction for Hyperspectral Analysis in Forensic Entomology.Insects · 2026Article
- Article
- Evaluation of the Usefulness of Machine Learning and Artificial Intelligence on Hyperspectral Images in the Diagnosis of Myelodysplastic Syndrome.Journal of biophotonics · 2026Article
- Hyperspectral imaging system for multiplexed cancer marker detection: design and clinical evaluation.Journal of biomedical optics · 2026Article
- Intraoperative Hyperspectral Imaging for Perfusion Assessment and Emerging Decision Support in Abdominal Surgery: A Systematic Review of Clinical Studies.Diagnostics (Basel, Switzerland) · 2026Review
- Design and Development of an Automated Pipeline for Medical Hyperspectral Image Acquisition, Processing, and Fusion.Journal of imaging · 2026Article
- Article
- Quantum Dots: Catalysts for a New Era of Precision Medicine and Biomedical Innovation.Journal of fluorescence · 2026Review
- Article
- Breast cancer-related lymphedema of the upper limb: integrating early surveillance and functional surgery into a synergistic management paradigm.Frontiers in surgery · 2026Review
- Dual-band spectral filter array integrated with a telecentric lens for real-time surface plasmon resonance sensing and imaging.Nanophotonics (Berlin, Germany) · 2025Article
- Detection of flap malperfusion after microsurgical tissue reconstruction using hyperspectral imaging and machine learning.Scientific reports · 2025Article
- DBY-Tobacco: a dual-branch model for non-tobacco related materials detection based on hyperspectral feature fusion.Frontiers in plant science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.