A foundation model for generalizable disease detection from retinal images

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.</p

A foundation model for generalizable disease detection from retinal images

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging

Real-world efficacy and safety of Ledipasvir plus Sofosbuvir and Ombitasvir/Paritaprevir/Ritonavir +/- Dasabuvir combination therapies for chronic hepatitis C: A Turkish experience

Background/Aims: This study aimed to evaluate the real-life efficacy and tolerability of direct-acting antiviral treatments for patients with chronic hepatitis C (CHC) with/without cirrhosis in the Turkish population.Material and Methods: A total of 4,352 patients with CHC from 36 different institutions in Turkey were enrolled. They received ledipasvir (LDV) and sofosbuvir (SOF)+/- ribavirin (RBV) ombitasvir/paritaprevir/ritonavir +/- dasabuvir (PrOD)+/- RBV for 12 or 24 weeks. Sustained virologic response (SVR) rates, factors affecting SVR, safety profile, and hepatocellular cancer (HCC) occurrence were analyzed.Results: SVR12 was achieved in 92.8% of the patients (4,040/4,352) according to intention-to-treat and in 98.3% of the patients (4,040/4,108) according to per-protocol analysis. The SVR12 rates were similar between the treatment regimens (97.2%-100%) and genotypes (95.6%-100%). Patients achieving SVR showed a significant decrease in the mean serum alanine transaminase (ALT) levels (50.90 +/- 54.60 U/L to 17.00 +/- 14.50 U/L) and model for end-stage liver disease (MELD) scores (7.51 +/- 4.54 to 7.32 +/- 3.40) (p<0.05). Of the patients, 2 were diagnosed with HCC during the treatment and 14 were diagnosed with HCC 37.0 +/- 16.0 weeks post-treatment. Higher initial MELD score (odds ratio [OR]: 1.92, 95% confidence interval [CI]: 1.22-2.38; p=0.023]), higher hepatitis C virus (HCV) RNA levels (OR: 1.44, 95% CI: 1.31-2.28; p=0.038), and higher serum ALT levels (OR: 1.38, 95% CI: 1.21-1.83; p=0.042) were associated with poor SVR12. The most common adverse events were fatigue (12.6%), pruritis (7.3%), increased serum ALT (4.7%) and bilirubin (3.8%) levels, and anemia (3.1%).Conclusion: LDV/SOF or PrOD +/- RBV were effective and tolerable treatments for patients with CHC and with or without advanced liver disease before and after liver transplantation. Although HCV eradication improves the liver function, there is a risk of developing HCC.Turkish Association for the Study of The Liver (TASL