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Diagnostic Study

Exploring artificial neural network combined with laser-induced auto-fluorescence technology for noninvasive in vivo upper gastrointestinal tract cancer early diagnosis

Authors:

Zheng Yi Chen,

Gastroenterology Department, Haikou People’s Hospital, Haikou City, Hainan Province, CN
About Zheng Yi

MBBS

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Sheng Fu ,

Division of Medicine, Singapore KK Women’s and Children’s Hospital, 100 Bukit Timah Road, Singapore 229899, SG
About Sheng

MBBS, PhD

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Minghui Li,

James Watt School of Engineering, University of Glasgow, Glasgow, GB
About Minghui

BSc, MSc, PhD

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Wei Zhang,

Department of Anaesthesia, Haikou People’s Hospital, Haikou City, Hainan Province, CN
About Wei

MBBS

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Hui Bin Ou

Department of Medical Engineering, Haikou People’s Hospital, Haikou City, Hainan Province, CN
About Hui Bin

BSc

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Abstract

In this study, a laser-induced auto-fluorescence (LIAF) system combined with the artificial neural network (ANN) algorithm is developed for early detection of human upper gastrointestinal tract carcinoma in vivo, through investigating the LIAF spectrum characteristics of the normal mucosa layer and the changes concerning an abnormal surface. Of the 44 participating patients, 41 underwent biopsy at the abnormal surface area at endoscopy. The ANN is employed to differentiate the LIAF data obtained from the normal and carcinoma patients (according to biopsy pathology diagnosis). The LIAF spectrum between 500 and 700 nm is selected and normalized. One data point is selected every 10 nm. A feed-forward back-propagation network with 2 hidden layers is constructed and trained. To evaluate the performance of ANN, 10 normal and 10 carcinoma data sets are tested with the trained ANN. 100% of the carcinoma data are very close to - 1 (desired), 80% of the normal surface is very close to 1 (desired), and 20% return values around -0.28. Previous works on this type of ANN suggested a threshold of - 0.5. As a result, all normal data are successful and the carcinoma cases are accurately classified and diagnosed. In conclusion, the LIAF technology combined with ANN diagnosis is more accurate.

How to Cite: Chen ZY, Fu S, Li M, Zhang W, Ou HB. Exploring artificial neural network combined with laser-induced auto-fluorescence technology for noninvasive in vivo upper gastrointestinal tract cancer early diagnosis. International Journal of Surgery: Oncology. 2019;5(1):e83. DOI: http://doi.org/10.1097/IJ9.0000000000000083
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Published on 20 Dec 2019.
Peer Reviewed

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