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

Head & Face Medicine

Matthias Gielisch¹  Daniel G. E. Thiem¹  Peer W. Kämmerer¹  Paul Römer¹  Bilal Al-Nawas²  Bastian Plaß³  Martin Schlüter³ 

[1] Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre Mainz, Augustusplatz 2, 55131, Mainz, Germany;International Scholar and Adjunct Associate Professor, Department of Oral and Maxillofacial Surgery, School of Dentistry, Kyung Hee University, Seoul, South Korea;School of Technology – Geoinformatics and Surveying, Institute for Spatial Information and Surveying Technology, University of Mainz - University of Applied Science, Mainz, Germany;

关键词: Sensoring;    Sensors;    Future medical;    Machine learning;    Artificial intelligence;    Non-invasive;    Non-contact;   

DOI  :  10.1186/s13005-021-00292-0

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY