Subh Bhattacharya, lead for healthcare, medical devices & sciences at Xilinx, examines how machine learning and artificial intelligence are impacting on medical imaging.
What is the outlook for Artificial Intelligence in Medical Imaging?
The use of artificial intelligence (AI) – including machine learning (ML) and deep learning techniques (DL) – is poised to become a transformational force in medical imaging. Patients, healthcare service providers, hospitals, medical equipment makers, pharmaceutical companies, professionals, and various stakeholders in the ecosystem all stand to benefit from ML driven tools. From anatomical geometric measurements, to cancer detection, to radiology, the possibilities are endless. In these scenarios, ML can lead to increased operational efficiencies, extremely positive outcomes and significant cost reduction.
What are some of the opportunities for Machine Learning in Medical Imaging?
There’s a broad spectrum of ways that ML can be used in medical imaging. For example, digital pathology, radiology, dermatology, vascular diagnostics and ophthalmology all use standard image processing techniques.
Chest x-rays are the most common radiological procedure with over two billion scans performed worldwide every year, that’s 548,000 scans a day. Such a huge quantity of scans imposes a heavy load on radiologists and taxes the efficiency of the workflow. Often ML, Deep Neural Network (DNN) and Convolutional Neural Networks (CNN) methods outperform radiologists in speed and accuracy, but the expertise of a radiologist is still of paramount importance. However, under stressful conditions during a fast decision-making process, human error rate could be as high as 30 per cent. Aiding the decision-making process with ML methods can improve the quality of result, providing the radiologists and other specialists an additional tool.
What is the regulatory attitude towards Machine Learning in Medical Imaging?
Regulatory support is steadily increasing and the US Federal Drug Administration (FDA) is approving more and more ML methods for diagnostic assistance and other applications. The FDA has also created a new regulatory framework for ML based products. This new framework refers to ML techniques as “Software as a Medical Device” (SaMD) and envisions significant benefits to quality and efficiency of care. To support this initiative, the FDA introduced a “predetermined change control plan” in premarket submissions which would include the types of anticipated modifications and the associated methodology to be used to implement those changes in a controlled manner.
The FDA expects commitments from medical device manufacturers on transparency and real-world performance monitoring for SaMD, as well as periodic updates on changes that were implemented as part of the approved pre-specifications and the algorithm change protocol. This framework enables the FDA and the manufacturers to monitor a product from its premarket development to post market performance and allows the regulatory oversight to embrace the iterative improvement power of an SaMD, while assuring patient safety.
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