Exactech Launches Predict+, First Machine Learning-Based Software that Informs Surgeons with Patient-Specific Outcomes Predictions After Shoulder Replacement Surgery
Exactech Expands Active Intelligence Portfolio of Smart Solutions
Exactech, a developer and producer of innovative implants, instrumentation and the Active Intelligence platform of technologies for joint replacement surgery, announced the launch of Predict+, a data-driven, clinical decision support tool that uses machine learning to predict individual patient outcomes after shoulder replacement surgery to assist surgeon decision making.
The software is designed to better inform surgeons regarding the expected outcomes that can be achieved after shoulder arthroplasty, based on the clinical experience documented within the world’s largest single-shoulder prosthesis outcomes database, consisting of more than 10,000 patients.
“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.”
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. In addition, it compares predictive results for anatomic and reverse shoulder arthroplasty at multiple post-operative timepoints. This guidance can help the surgeon better personalize patient treatment by identifying factors that drive the outcome predictions, including modifiable factors such as the patient losing weight, quitting smoking, and completing pre-habilitation. Finally, Predict+ aggregates the 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 after anatomic and reverse shoulder replacement.
Developed in partnership with KenSci, Predict+ is a first-of-its-kind work that showcases the predictive power of machine learning to transform healthcare. The resultant software builds on previously published, peer-reviewed research in the field.
“Machine learning models used within Predict+ have been applied and accelerated by 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.”
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