Machine Learning Will Reshape Diagnostic Medicine


Diagnosing disease is one of the more labor-intensive aspects of the healthcare system. It also happens to be one that is particularly well-suited to being performed by machine learning algorithms. While work in this area is in its early stages, the technology is evolving rapidly and appears poised to transform diagnostic medicine.

Thanks largely to the huge volumes of data collected from patients, medical diagnostics is an ideal domain for machine learning. Much of the diagnostic data is image-based, such as X-rays, MRI scans, and ultrasound imagery, but can also include things like genomic profiles, epidemiological data, blood tests, biopsy results, and even medical research papers. As a result, there is a wealth of data available for training neural networks and for other machine learning techniques.

There is also a critical need for quick, accurate diagnoses. The healthcare system cannot always provide this, at least not everywhere, or at a price point that is within reach of all patients. Machine learning promises to cut costs dramatically, while providing something approaching real-time diagnostics. And in an increasing number of instances, the software can deliver a more accurate diagnosis than that of a trained physician.

One very recent example of this is an application developed by South Korean researchers at Cheonan Public Health Center and Kyong Hwan Jin at the Korea Advanced Institute of Science and Technology. According to a recent report in MIT Technology Review, Hongyoon Choi and Hwan Jin built a deep convolutional neural network (CNN) that is able to accurately identify people who will develop Alzheimer’s disease within three years, based soley on a PET (positron emission tomography) brain scan.

Using a dataset of brain images of people with full-blown Alzheimer’s, those with mild cognitive impairment (MCI), and those with MCI that subsequently developed Alzheimer’s, Hongyoon and Kyong were able to train a CNN that could predict the disease with an accuracy of 84 percent. That’s much better than what a trained diagnostician could manage to do.

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