Thiem, Daniel G E; Romer, Paul; Gielisch, Matthias; Al-Nawas, Bilal; Schluter, Martin; Pla©¬, Bastian; Kammerer, Peer W
Head & face medicine
2021Sep ; 17 ( 1 ) :38.
PMID : 34479595
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Thiem, Daniel G E - Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre Mainz, Augustusplatz 2, 55131, Mainz, Germany. daniel.thiem@uni-mainz.de.
Romer, Paul - Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre Mainz, Augustusplatz 2, 55131, Mainz, Germany.
Gielisch, Matthias - Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre Mainz, Augustusplatz 2, 55131, Mainz, Germany.
Al-Nawas, Bilal - International Scholar and Adjunct Associate Professor, Department of Oral and Maxillofacial Surgery, School of Dentistry, Kyung Hee University, Seoul, South Korea.
Schluter, Martin - School of Technology - Geoinformatics and Surveying, Institute for Spatial Information and Surveying Technology, University of Mainz - University of Applied Science, Mainz, Germany.
Pla©¬, Bastian - School of Technology - Geoinformatics and Surveying, Institute for Spatial Information and Surveying Technology, University of Mainz - University of Applied Science, Mainz, Germany.
Kammerer, Peer W - Department of Oral and Maxillofacial Surgery, Facial Plastic Surgery, University Medical Centre Mainz, Augustusplatz 2, 55131, Mainz, Germany.
ABSTRACT
BACKGROUND: Hyperspectral 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.
METHODS: A 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).
RESULTS: The reflectance values differed significantly (p??80% each. CONCLUSION: Oral 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. CI - ??2021. The Author(s).
keyword
Artificial intelligence; Future medical; Machine learning; Non-contact; Non-invasive; Sensoring; Sensors
MESH
Artificial Intelligence, *Deep Learning, Humans, Hyperspectral Imaging, *Mouth Neoplasms/diagnostic imaging, Mucous Membrane, Muscles, Neural Networks, Computer
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