ArticleHead & face medicine2021
Hyperspectral imaging and artificial intelligence to detect oral malignancy - part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network.
Article in Head & face medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Classification of esophageal cancer by using hyperspectral data.International journal of computer assisted radiology and surgery · 2026Article
- AI-assisted image analysis ofBioinformation · 2026Article
- Enhancing Oral Health Diagnostics With Hyperspectral Imaging and Computer Vision: Clinical Dataset Study.JMIR medical informatics · 2025Article
- Intraoral perfusion assessment using endoscopic hyperspectral imaging (EHSI)- first description of a novel approach.Clinical oral investigations · 2025Article
- In-vivo non-contact multispectral oral disease image dataset with segmentation.Scientific data · 2024Article
- Flap Monitoring Techniques: A Review.Journal of clinical medicine · 2024Review
- The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.Frontiers in nutrition · 2024Article
- A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health.Diagnostics (Basel, Switzerland) · 2023Review
- Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- Comparison of Hyperspectral Imaging and Microvascular Doppler for Perfusion Monitoring of Free Flaps in an In Vivo Rodent Model.Journal of clinical medicine · 2022Article
- Trends in hyperspectral imaging: from environmental and health sensing to structure-property and nano-bio interaction studies.Analytical and bioanalytical chemistry · 2022Article
- New Approach to the Old Challenge of Free Flap Monitoring-Hyperspectral Imaging Outperforms Clinical Assessment by Earlier Detection of Perfusion Failure.Journal of personalized medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library.
methodsA total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN).
resultsThe reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each.
conclusionOral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.
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