QuRay
Medical imaging techniques, such as CT, MRI, and X-ray scans, have become an integral part of patient diagnosis and treatment around the globe. Although imaging technology has advanced significantly over the years, the human interpretation errors remain the same. Studies on average report a 3 – 5 % error rate in daily practice. However, when evaluating abnormal scans, there is an approximately one in three chance the medical problem is misdiagnosed even by an experienced radiologist.
Computer-aided detection and diagnosis methods are being introduced into medical practice by acting as a prospective peer review tool for radiologists. However, there is an upper limit to how large and complex the AI models can become. Reaching satisfactory accuracy and reliability consumes a lot of computational power and can take weeks to train. To deal with growing machine learning model complexity, future machine learning models will require computational power of a quantum computer to deliver results in real-time.
We investigated quantum machine learning capabilities in medical image analysis and as a result built a prototype application QuRay that uses quantum machine learning algorithms to detect 8 the most common radiographic findings in the chest X-rays. Explore the demo application to see how the solution works and how it can be integrated in the radiologist and practitioner workflow.
Medical imaging techniques, such as CT, MRI, and X-ray scans, have become an integral part of patient diagnosis and treatment around the globe. Although imaging technology has advanced significantly over the years, the human interpretation errors remain the same. Studies on average report a 3 – 5 % error rate in daily practice. However, when evaluating abnormal scans, there is an approximately one in three chance the medical problem is misdiagnosed even by an experienced radiologist.
Computer-aided detection and diagnosis methods are being introduced into medical practice by acting as a prospective peer review tool for radiologists. However, there is an upper limit to how large and complex the AI models can become. Reaching satisfactory accuracy and reliability consumes a lot of computational power and can take weeks to train. To deal with growing machine learning model complexity, future machine learning models will require computational power of a quantum computer to deliver results in real-time.
We investigated quantum machine learning capabilities in medical image analysis and as a result built a prototype application QuRay that uses quantum machine learning algorithms to detect 8 the most common radiographic findings in the chest X-rays. Explore the demo application to see how the solution works and how it can be integrated in the radiologist and practitioner workflow.
Medical imaging techniques, such as CT, MRI, and X-ray scans, have become an integral part of patient diagnosis and treatment around the globe. Although imaging technology has advanced significantly over the years, the human interpretation errors remain the same. Studies on average report a 3 – 5 % error rate in daily practice. However, when evaluating abnormal scans, there is an approximately one in three chance the medical problem is misdiagnosed even by an experienced radiologist.
Computer-aided detection and diagnosis methods are being introduced into medical practice by acting as a prospective peer review tool for radiologists. However, there is an upper limit to how large and complex the AI models can become. Reaching satisfactory accuracy and reliability consumes a lot of computational power and can take weeks to train. To deal with growing machine learning model complexity, future machine learning models will require computational power of a quantum computer to deliver results in real-time.
We investigated quantum machine learning capabilities in medical image analysis and as a result built a prototype application QuRay that uses quantum machine learning algorithms to detect 8 the most common radiographic findings in the chest X-rays. Explore the demo application to see how the solution works and how it can be integrated in the radiologist and practitioner workflow.