Point‐of‐care detection of fibrosis in liver transplant surgery using near‐infrared spectroscopy and machine learning

Abstract

IntroductionVisual 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.MethodsWe undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors.ResultsLiver 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).ConclusionThis study demonstrates that a point‐of‐care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia

“…Another point to consider is that the ML model design can be easily deployed to a cloud-based infrastructure, serving different geographic regions in real time and on a large scale. Thus, NIR spectroscopy associated with machine learning in medical practice can safely improve decision making, reducing the public health system resource burden and improving patients' quality of life [25][26][27].…”

“…Integrating NIR spectroscopy with machine learning as a screening tool in medical diagnostics can have significant implications for healthcare [26,27]. The proposed NIR + ML screening tool can enable early detection of neutropenia by analyzing subtle changes in biological fingertip tissue that may precede noticeable symptoms.…”