Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning

Jia Meng; Guangxing Wang; Lingxi Zhou; Shenyi Jiang; Shuhao Qian; Lingmei Chen; Chuncheng Wang; Rushan Jiang; Chen Yang; Bo Niu; Yijie Liu; Zhihua Ding; Shuangmu Zhuo; Zhiyi Liu

doi:10.1117/1.JBO.28.4.045001

8 April 2023 Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning

Jia Meng, Guangxing Wang, Lingxi Zhou, Shenyi Jiang, Shuhao Qian, Lingmei Chen, Chuncheng Wang, Rushan Jiang, Chen Yang, Bo Niu, Yijie Liu, Zhihua Ding, Shuangmu Zhuo, Zhiyi Liu

Author Affiliations +

Journal of Biomedical Optics, Vol. 28, Issue 4, 045001 (April 2023). https://doi.org/10.1117/1.JBO.28.4.045001

Abstract

Significance

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.

Aim

Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.

Approach

Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT).

Results

The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively.

Conclusions

The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Jia Meng, Guangxing Wang, Lingxi Zhou, Shenyi Jiang, Shuhao Qian, Lingmei Chen, Chuncheng Wang, Rushan Jiang, Chen Yang, Bo Niu, Yijie Liu, Zhihua Ding, Shuangmu Zhuo, and Zhiyi Liu "Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning," Journal of Biomedical Optics 28(4), 045001 (8 April 2023). https://doi.org/10.1117/1.JBO.28.4.045001

Received: 23 December 2022; Accepted: 26 March 2023; Published: 8 April 2023

ACCESS THE FULL ARTICLE

JOURNAL ARTICLE
14 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY