1. Point‐of‐care detection of fibrosis in liver transplant surgery using near‐infrared spectroscopy and machine learning
- Author
-
Varun J. Sharma, John A. Adegoke, Michael Fasulakis, Alexander Green, Su K. Goh, Xiuwen Peng, Yifan Liu, Louise Jackett, Angela Vago, Eric K. W. Poon, Graham Starkey, Sarina Moshfegh, Ankita Muthya, Rohit D'Costa, Fiona James, Claire L. Gordon, Robert Jones, Isaac O. Afara, Bayden R. Wood, and Jaishankar Raman
- Subjects
chemometrics ,liver ,near‐infrared spectroscopy ,spectromics ,transplant ,vibrational spectroscopy ,Medicine - Abstract
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. Methods We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors. 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 (R2 = 0.842) and mature (R2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively). Conclusion This study demonstrates that a point‐of‐care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.
- Published
- 2023
- Full Text
- View/download PDF