Exactech further enhanced its Active Intelligence® platform of technologies for joint replacement with launch of Predict+™, a data-driven, clinical decision support tool that uses machine learning to predict individual patient outcomes after shoulder replacement surgery.
The software is based on clinical experience documented within the world’s largest single-shoulder prosthesis outcomes database, comprising more than 10,000 patients. It supports the Equinoxe® shoulder and ExactechGPS® guided personalized surgery system.
With Predict+, the surgeon inputs as few as 19 data points about a patient and within minutes, the software predicts the patient’s potential outcomes, including pain and range of motion, based on the results reported by patients with similar age, gender and prosthesis type.
Further, Predict+ compares predictive results for anatomic and reverse shoulder arthroplasty at multiple post-operative timepoints. This can help the surgeon better personalize patient treatment by identifying factors that drive outcome predictions, including modifiable factors such as the patient losing weight, quitting smoking and completing pre-habilitation.
Predict+ then aggregates outcomes and complications within the database so that surgeons and patients can compare their personalized predictions with the clinical experience of patients of similar age and gender.
“Machine learning models used within Predict+ have been applied and accelerated by [co-developer] KenSci’s AI Platform for Digital Health,” said Vikas Kumar, Ph.D., Principal Data Science Lead for Innovation and Devices at KenSci. “We are witnessing an unprecedented development in computer science to assist hundreds of surgeons globally in improving post-surgical outcomes. This is just the beginning.”
“Predict+ is a new application of clinical research that represents a significant advancement in the patient consultation process,” said Chris Roche, Exactech’s Vice President of Extremities.“Using machine learning analyses, Predict+ delivers personalized, evidence-based predictions that objectively quantify the risk and benefit that an individual patient may experience after anatomic and reverse shoulder replacement and aligns patient and surgeon expectations in order to improve patient satisfaction.”