Although the technology is not yet approved for routine clinical use, it’s constantly improving and aims to address the limitations inherent in today’s pathology processes.
“You do a biopsy, but instead of having a pathologist read it with their very rough scores of stage 1, 2, or 3, you read it by an AI-driven machine that can quantify it with a score of 1.5 or 1.75 instead of 1 or 2,” Vlad Ratziu, MD, PhD, professor of hepatology at the Sorbonne Université and Hôpital Pitié-Salpêtrière Medical School in Paris, France, and coeditor of The Journal of Hepatology, said in an interview.
“The technology is automated, more sensitive to change, and more highly quantitative. It has implications for liver disease diagnoses, clinical trials, and treatments,” added Dr. Ratziu, who has written about the promise and challenges inherent in developing treatments for metabolic dysfunction–associated steatotic liver disease (MASLD).
To explore the potential impact of AI-powered technologies for the clinic, this news organization spoke with representatives from three companies identified by Dr. Ratziu as leaders in the field: HistoIndex, PathAI, and PharmaNest. Each company uses proprietary technology augmented by AI, and their tools have been used in published trials.
Moving Toward Better Diagnoses and Disease Management
The traditional approach for staging liver fibrosis relies on trained pathologists manually evaluating stained tissue samples obtained from biopsies of the liver.
But this method, though still considered the gold standard, doesn’t always provide the granularity needed for an accurate diagnosis or a reliable assessment in clinical trials, said Dean Tai, PhD, HistoIndex’s cofounder and chief scientific officer.
Although noninvasive tests (NITs), alone or with traditional histologic examination, are increasingly used during clinical management because they are less invasive and more repeatable for disease monitoring, they are limited in their precision and ability to provide comprehensive information, Dr. Tai said. That’s because “no single NIT or single-dimensional measurement of a biomarker offers a full assessment of disease activity, fibrogenic drive, and fibrosis load.”
In contrast, AI provides “a highly reproducible and objective assessment of liver fibrosis severity,” he said. “It eliminates the variability associated with staining methods, while revealing changes in the nano-architecture and morphology of collagen fibers not discernible by the human eye or current NITs, especially in the early stages of fibrosis or in cases of simultaneous progression and regression.”
Mathieu Petitjean, PhD, founder and CEO of PharmaNest, has a similar view.
Although degree of liver fibrosis is associated with long-term outcomes of patients with MASLD, “poor detection thresholds due to their categorical nature mean that small and relevant changes are not reflected by changes in staging,” he said. “The reliable detection [with AI] of subtle changes in the phenotypes of fibrosis will significantly enrich the understanding of progression and regression of fibrosis severity.”
The ability of AI-based tools to see patterns the human eye cannot also means they could “help in predicting which patient may respond to a drug, in order to get the right treatments to the right patients as soon as possible,” said Katy Wack, PhD, vice president of clinical development at PathAI.
“Additionally, AI-based algorithms have been developed to provide more quantitative continuous scores to better capture change and discover new tissue-based biomarkers, which may be prognostic or predictive of clinical benefit,” she said.
Such tools are currently undergoing testing and validation for use in trials and diagnostically.
The standardization and reproducibility offered by AI-driven technology could facilitate more consistent diagnoses across different healthcare settings, Dr. Tai suggested. “As the integration of the technology with other blood-, imaging-, and omics-based techniques evolves, it may enable earlier detection of liver diseases, more accurate monitoring of disease progression, and better evaluation of treatment responses, ultimately improving patient care and outcomes.”