Sunday, November 27 | 2:30 p.m.-3:30 p.m. | S5-SSGI03-4 | Room S406B
An artificial intelligence (AI) algorithm can yield highly accurate performance for staging liver fibrosis on CT exams, according to this scientific presentation.Researchers led by presenter Dr. Alessandro Furlan of the Pittsburgh Liver Center in Pittsburgh, PA, retrospectively gathered a dataset of 819 patients with liver fibrosis and who had received portal-venous phase CT exams for liver fibrosis staging between January 2015 and January 2022. Of these, 623 were used to train algorithms and 196 were set aside for testing.
The researchers developed three AI models. The first was based only on liver analysis, while the second only analyzed the spleen. The last algorithm, which combined both liver and spleen analysis, yielded the best results.
In testing on 196 patients, the combined model outperformed both radiologists and serum tests. Furthermore, the diagnostic performance of the combined model wasn’t influenced by patient characteristics, pathology, and CT data.
“Application of this segmentation and classification algorithm may help clinicians in preoperative planning for liver surgery, therapeutic effect evaluation, and [predicting] the prognosis of chronic liver disease, without excess burden,” the authors wrote.
Want to know more? Attend this talk on Sunday afternoon.
![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&dpr=2&fit=crop&h=167&q=70&w=250)
