At the Hospital for Special Surgery in New York City, our team recently completed an exciting computer vision project aimed at revolutionizing the way orthopedic X-ray images are analyzed. The primary goal was to accurately detect and measure various anatomical angles, such as the coxa-profunda, coxa-protrusion, and the lateral centeral-edge angle, which play a crucial role in orthopedic assessments.
Prevailing challenge
In today’s fast-paced medical landscape, leveraging technology to improve patient outcomes has become very important. A recent project that is a perfect example of this shift towards technological integration is the computer vision initiative spearheaded at the Hospital for Special Surgery in New York City. The core objective of this innovative venture was to enhance the detection of various orthopedic angles in medical X-ray images, a crucial aspect in the diagnosis and treatment planning for musculoskeletal conditions.
Our innovative solution
Addressing this challenge, our dedicated R&D team set on a journey to construct a robust, production-grade pipeline capable of converting vast amounts of raw computer vision data into formats intelligible to sophisticated ML algorithms. Through painstaking data annotation, we ensured the adaptability and scalability of our models for a broad spectrum of future applications in the span of a three to four-month journey.
In the landscape of medical-based machine learning models, our journey was comprehensive, exploring and harnessing advanced computational techniques, notably those involving cosine and sine value analyses. The culmination of this exhaustive research and development phase was the creation of a revolutionary model. This model demonstrated an impressive 95% accuracy rate in the detection of orthopedic angles, setting a new benchmark for precision in the field.
The impact
The integration of our state-of-the-art machine learning model into the diagnostic workflow has dramatically transformed the orthopedic imaging landscape. Processes that once spanned several days are now completed in a fraction of the time, significantly enhancing operational efficiency and patient throughput.
This leap in diagnostic capability has not only fastened the analysis process but also expanded the volume of X-rays that can be evaluated daily. Doctors, who were previously capped at reviewing 15-16 X-rays per day, can now analyze between 50 to 60, thanks to the accuracy and speed afforded by our innovative technology.
As we refine our algorithms and explore new applications, we aim to further integrate machine learning into healthcare, continually improving outcomes and operational efficiency.