ArticleJournal of biomedical optics2023
Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning.
Article in Journal of biomedical optics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Interpreting tissue stiffening with lung tumorigenesis by imaging architectural resembling of extracellular matrix components.Communications biology · 2026Article
- Decoding elastin-collagen resemblance in keloid scar through label-free imaging and machine learning.Journal of biomedical optics · 2026Article
- Mechanistic insights into Strontium-90 superficial radiotherapy in keloids: senescence-associated changes and ECM remodeling across cell-matrix scales.Frontiers in pharmacology · 2026Article
- Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.BMC cancer · 2025Article
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- The diagnostic value and associated molecular mechanism study for fibroblast-related mitochondrial genes on keloid.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- The relationship between the collagen score at the anastomotic site of esophageal squamous cell carcinoma and anastomotic leakage.Journal of thoracic disease · 2024Article
- Investigating cartilage-related diseases by polarization-resolved second harmonic generation (P-SHG) imaging.APL bioengineering · 2024Article
- Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.