Machine learning application to histology for the study of cholangiopathies (BiliQML): A chance to put liver biopsy back to its former glory?

Camilla Venturin¹, Luca Fabris^{1

2}

Affiliations

¹ Clinical Medicine 1 and Thrombotic and Haemorrhagic Disease Unit, and Haemophilia Center, Padua University-Hospital, Department of Medicine (DIMED), University of Padua, Padua, Italy.
² Department of Internal Medicine, Digestive Disease Section, Liver Center, Yale University, New Haven, Connecticut, United States.

Free article

Comment

Camilla Venturin et al. Am J Physiol Gastrointest Liver Physiol. 2024.

Free article

. 2024 Dec 1;327(6):G733-G736.

doi: 10.1152/ajpgi.00173.2024. Epub 2024 Oct 8.

Camilla Venturin¹, Luca Fabris^{1

2}

¹ Clinical Medicine 1 and Thrombotic and Haemorrhagic Disease Unit, and Haemophilia Center, Padua University-Hospital, Department of Medicine (DIMED), University of Padua, Padua, Italy.
² Department of Internal Medicine, Digestive Disease Section, Liver Center, Yale University, New Haven, Connecticut, United States.

No abstract available

Keywords: cholangiocyte; cholangiopathies; ductular reaction; liver biopsy; machine learning.

Comment on

BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images.
Hellen DJ, Fay ME, Lee DH, Klindt-Morgan C, Bennett A, Pachura KJ, Grakoui A, Huppert SS, Dawson PA, Lam WA, Karpen SJ. Hellen DJ, et al. Am J Physiol Gastrointest Liver Physiol. 2024 Jul 1;327(1):G1-G15. doi: 10.1152/ajpgi.00058.2024. Epub 2024 Apr 23. Am J Physiol Gastrointest Liver Physiol. 2024. PMID: 38651949 Free PMC article.