Back to Search
Start Over
Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning.
- Source :
-
Health science reports [Health Sci Rep] 2023 Oct 31; Vol. 6 (11), pp. e1652. Date of Electronic Publication: 2023 Oct 31 (Print Publication: 2023). - Publication Year :
- 2023
-
Abstract
- Introduction: Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3-s scans using a handheld near-infrared-spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples.<br />Methods: We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors.<br />Results: Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20-60) and mature fibrosis of 30% (10%-50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%-15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial-least square regression machine learning, this study predicted the percentage of both immature ( R <superscript>2</superscript> = 0.842) and mature ( R <superscript>2</superscript> = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively).<br />Conclusion: This study demonstrates that a point-of-care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.<br />Competing Interests: The authors declare no conflict of interest.<br /> (© 2023 The Authors. Health Science Reports published by Wiley Periodicals LLC.)
Details
- Language :
- English
- ISSN :
- 2398-8835
- Volume :
- 6
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- Health science reports
- Publication Type :
- Academic Journal
- Accession number :
- 37920655
- Full Text :
- https://doi.org/10.1002/hsr2.1652