Precision Post-Surgical Healing Estimator with Multimodal Image and Health History Analysis

It is a very complicated task to predict post-surgical recovery as it is a process that depends on various factors related to patients, including their wound level of severity, comorbidity, age, and lifestyle. Malpractice may result in failure to review the conditions and, hence, undergo remedial measures and take a long time to heal. To reduce these complexities, the current paper makes a multimodal AI-based framework comprising the convolutional neural networks (CNNs) (to analyze the wounds) and the structured electronic health records (EHRs) (to provide individual recovery period estimations and warnings of complications). The system implements a fusion model based on visual characteristics and clinical information and then applying a regression model to estimate healing duration and a time-series LSTM model to iteratively-produce effective healing curves. A recommendation engine gives care advice that is informed by medical best practices and an alert module identifies deviations that are indicative of infection or stagnation. System training and testing were based on a multimodal database, including high-resolution wound images and anonymized clinical profile. The experimental findings show the mean absolute error of 2.8 days when predicting the recovery, 93.2 percent accuracy in identifying complications, and the F1-score of 93.1 percent. The platform has an interface that is patient friendly and aimed at improving remote monitoring and decision making. This paradigm supports the pro-active follow up care taken after a surgery, minimizes hospital reliance, and puts the patients in charge of recovery at home.