An artificial intelligence model to predict hepatocellular carcinoma risk in Korean and Caucasian patients with chronic hepatitis B.

BACKGROUND & AIMS: Several models have recently been developed to predict risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). Our aims were to develop and validate an artificial intelligence-assisted prediction model of HCC risk. METHODS: Using a gradient-boosting machine (GBM) algorithm, a model was developed using 6,051 patients with CHB who received entecavir or tenofovir therapy from 4 hospitals in Korea. Two external validation cohorts were independently established: Korean (5,817 patients from 14 Korean centers) and Caucasian (1,640 from 11 Western centers) PAGE-B cohorts. The primary outcome was HCC development. RESULTS: In the derivation cohort and the 2 validation cohorts, cirrhosis was present in 26.9%-50.2% of patients at baseline. A model using 10 parameters at baseline was derived and showed good predictive performance (c-index 0.79). This model showed significantly better discrimination than previous models (PAGE-B, modified PAGE-B, REACH-B, and CU-HCC) in both the Korean (c-index 0.79 vs. 0.64-0.74; all p <0.001) and Caucasian validation cohorts (c-index 0.81 vs. 0.57-0.79; all p <0.05 except modified PAGE-B, p = 0.42). A calibration plot showed a satisfactory calibration function. When the patients were grouped into 4 risk groups, the minimal-risk group (11.2% of the Korean cohort and 8.8% of the Caucasian cohort) had a less than 0.5% risk of HCC during 8 years of follow-up. CONCLUSIONS: This GBM-based model provides the best predictive power for HCC risk in Korean and Caucasian patients with CHB treated with entecavir or tenofovir. LAY SUMMARY: Risk scores have been developed to predict the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B. We developed and validated a new risk prediction model using machine learning algorithms in 13,508 antiviral-treated patients with chronic hepatitis B. Our new model, based on 10 common baseline characteristics, demonstrated superior performance in risk stratification compared with previous risk scores. This model also identified a group of patients at minimal risk of developing HCC, who could be indicated for less intensive HCC surveillance.

Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?

BACKGROUND: Osteoarthritis (OA) is the leading cause of disability among adults in the United States. As the diagnosis is based on the accurate interpretation of knee radiographs, use of a convolutional neural network (CNN) to grade OA severity has the potential to significantly reduce variability. METHODS: Knee radiographs from consecutive patients presenting to a large academic arthroplasty practice were obtained retrospectively. These images were rated by 4 fellowship-trained knee arthroplasty surgeons using the International Knee Documentation Committee (IKDC) scoring system. The intraclass correlation coefficient (ICC) for surgeons alone and surgeons with a CNN that was trained using 4755 separate images were compared. RESULTS: Two hundred eighty-eight posteroanterior flexion knee radiographs (576 knees) were reviewed; 131 knees were removed due to poor quality or prior TKA. Each remaining knee was rated by 4 blinded surgeons for a total of 1780 human knee ratings. The ICC among the 4 surgeons for all possible IKDC grades was 0.703 (95% confidence interval [CI] 0.667-0.737). The ICC for the 4 surgeons and the trained CNN was 0.685 (95% CI 0.65-0.719). For IKDC D vs any other rating, the ICC of the 4 surgeons was 0.713 (95% CI 0.678-0.746), and the ICC of 4 surgeons and CNN was 0.697 (95% CI 0.663-0.73). CONCLUSIONS: A CNN can identify and classify knee OA as accurately as a fellowship-trained arthroplasty surgeon. This technology has the potential to reduce variability in the diagnosis and treatment of knee OA.