Abstract: A system and method for detecting and predicting surgical wound infections is disclosed. The method includes receiving wound site data, ambient data, reference site data and auxiliary data from one or more wearable devices, one or more sensing units or a combination thereof, a patient history data from electronic health record database, patient assessment data from a clinical professional, information associated with a patient from the patient or any combination thereof. The method further includes determining one or more desired parameters and extracting one or more features from the one or more desired parameters. The method includes applying the extracted one or more features into a trained Artificial Intelligence (AI) based data model and a trained statistical model for the patient. Further, the method includes detecting presence of an infection, predicting likelihood of infection and generating a wound infection score.

Abstract: A system and method for detecting and predicting surgical wound infections is disclosed. The method includes receiving wound site data, ambient data, reference site data and auxiliary data from one or more wearable devices, one or more sensing units or a combination thereof, a patient history data from electronic health record database, patient assessment data from a clinical professional, information associated with a patient from the patient or any combination thereof. The method further includes determining one or more desired parameters and extracting one or more features from the one or more desired parameters. The method includes applying the extracted one or more features into a trained Artificial Intelligence (AI) based data model and a trained statistical model for the patient. Further, the method includes detecting presence of an infection, predicting likelihood of infection and generating a wound infection score.

Abstract: A system and method for detecting and predicting surgical wound infections is disclosed. The method includes receiving wound site data, ambient data, reference site data and auxiliary data from one or more wearable devices, one or more sensing units or a combination thereof, a patient history data from electronic health record database, patient assessment data from a clinical professional, information associated with a patient from the patient or any combination thereof. The method further includes determining one or more desired parameters and extracting one or more features from the one or more desired parameters. The method includes applying the extracted one or more features into a trained Artificial Intelligence (AI) based data model and a trained statistical model for the patient. Further, the method includes detecting presence of an infection, predicting likelihood of infection and generating a wound infection score.