Ebook

Chapter 2

AI to Enable Early Diagnosis and Support Clinical Decisions

A recent report found that medical errors were the third leading cause of death in the US [2]. Most of the errors are diagnostic, including misdiagnosis and diagnoses never given to patients. Most people in the United States experience at least one such misdiagnosis in their lifetime, and 10% of cases result in death [2] [3].

With AI, information processing and decision making become more efficient and less error prone. The examples below illustrate how AI-based devices can help healthcare providers deliver improved diagnoses directly from medical images, physiological signals, or patient health records.

An AI-based real-time map of the heart and its electrical activity helps doctors pinpoint surgical interventions for atrial fibrillation. (Image credit: Corify Care)

Assessing the Risk of Falls in Older Adults with Inertial Sensors and Machine Learning

Challenge

There are almost one in three adults over the age of 65 who fall each year, making falling the main cause of fatal and nonfatal injuries in this age group.

Solution

Kinesis Health Technologies developed a device called QTUG™ (Quantitative Timed Up and Go), an objective, quantitative method of screening for fall risk, frailty, and mobility impairment using wireless inertial sensors placed on a patient’s leg. The final product uses AI-based models developed with MATLAB to compute a fall risk estimate (FRE) and a frailty index (FI).

  • In a QTUG test, a patient is fitted with two wireless inertial sensors, one on each leg below the knee. Each sensor includes an accelerometer and a gyroscope.
  • To remove high-frequency noise in the data collected from these sensors, they used digital filters they designed using the Filter Designer in Signal Processing Toolbox™.
  • The team used Statistics and Machine Learning Toolbox™ to select the subset of features with the highest predictive value and validate a regularized discriminant classifier model implemented in MATLAB.
  • The team trained their models on clinical trial data collected on thousands of patients and assessed the results produced by the combined classifier.
  • To update the classifier coefficients based on a new reference data set, the engineers export them from MATLAB to a resource file that is then incorporated into their build.
A log of metrics collected from a timed up and go test.

Patient quantitative metrics. (Image credit: Kinesis Health Technologies)

Results

To date, clinicians in eight countries have used QTUG to evaluate more than 20,000 patients. The team continues to improve the reference data set as new results come in. The team estimates that its development time was three times faster than developing in Java®, reducing the time to market and registration as a Class I device with the US FDA, Health Canada, and the European Medicines Agency (EMA).

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References

[2] Makary, Martin A, and Michael Daniel. “Medical Error—the Third Leading Cause of Death in the US.” BMJ, May 3, 2016. https://doi.org/10.1136/bmj.i2139.

[3] Balogh, Erin, Bryan T. Miller, and John Ball. Improving Diagnosis in Health Care. Washington, DC: The National Academies Press, 2015.