Liver disease continues to be a notable Worldwide health concern, with numerous patients receiving diagnoses at a delayed stage, with far reaching adverse consequences including higher rates of reaction requiring intensive medical treatments or death. Traditionally, diagnosis depended out on manual examination of medical parameters and imaging which is all time taking process and thus may cause errors as they are of human. This is compounded further by limited availability of experienced radiologists and expert diagnostic tools in areas. The current study meets the requirement of a reliable, clinically translatable liver disease prediction algorithm incorporating various diagnostic tools within limited time and resource constraints. LiverCare AI: Our livercareAI, developed as a Flask web application, offers two ways of prediction; using your body parameters (on test values) and by looking at the image for representations from the scan format(ultrasound/CT/MRI). Aided by machine learning algorithms trained on a dataset of Indian liver patient records, the system also incorporates image classification models that deliver high-confidence predictions. It features a modern, responsive user interface with educational resources on liver health and personalized recommendations according to the prediction results.First efficacy testing shows a highly predictive ability and amazing performance in both parameter-based and image-based settings. This approach combines the processing of clinical data with AI-based imaging detection, providing earlier diagnosis and reducing diagnostic delays. This provides an adjustable and broad resource-limited telemedicine integration solution
Mrs. Subhashree D C (Mon,) studied this question.