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Emerging research suggests the machine learning-based DiaBeats algorithm could facilitate early detection of prediabetes or diabetes.
Could artificial intelligence (AI) enhance the ability of clinicians to detect prediabetes and diabetes?
Findings from a recently published study show that adjunctive use of DiaBeats, a machine learning-based algorithm, with electrocardiogram (ECG) recordings may provide an alternative noninvasive imaging approach to screening for prediabetes and diabetes.
“In theory, our study provides a relatively inexpensive, non-invasive, and accurate alternative (to current diagnostic methods) which can be used as a gatekeeper to effectively detect diabetes and prediabetes early in its course,” wrote study co-author Anoop R. Kulkarni, Ph.D., who is affiliated with Innotomy Consulting in Bengaluru, India, anbd colleagues. “Nevertheless, adoption of this algorithm into routine practice will need robust validation on external, independent data sets.”
As the intrinsic risk of prediabetes has become increasingly recognized by the medical community, identification of low-cost, noninvasive methods for diagnosis or risk stratification for prediabetes and future diabetes has become paramount. With this in mind, a team from the Late Medical Research Foundation in Nagpur, India and M&H Research LLC sought to assess whether leveraging machine learning could identify prediabetes or diabetes from noninvasive cardiovascular imaging as the cardiovascular system bears some of the earliest signs of damage from the diabetic process.
To do so, investigators designed their study as an analysis using data from the Diabetes in Sindhi Families in Nagpur (DISFIN) study. A cross-sectional observational study aimed at estimating prevalence of type 2 diabetes in Sindhi families of Nagpur, 1262 of the 1462 individuals enrolled in the DISFIN study were included in the current analysis. From these 1262 individuals, investigators obtained data related to 10,461 time-aligned heartbeats recorded digitally.
This cohort had a mean age of 48 years and 61 percent were female. The overall prevalence of type 2 diabetes, prediabetes, and insulin resistance was 30 percent, 14 percent, and 35 percent respectively. Investigators also pointed out hypertension, obesity, and dyslipidemia were present among 51 percent, 40 percent, and 36 percent of participants.
For the purpose of analysis, the 10,461 time-aligned heartbeats were split into a training set, a validation set, and an independent test set, which included 8892, 523, and 1,046 beats, respectively. These recordings were processed with median filtering, band-pass filtering, and standard scaling. Investigators pointed out minority oversampling was performed to balance the training dataset prior to initiation of training and extreme gradient boosting was used to train the classifier that used the signal-processed ECG as input and predicted the membership to no diabetes, prediabetes, or type 2 diabetes classes.
Upon analysis, investigators found the DiaBeats algorithm predicted the classes with 97.1 percent precision, 96.2 percent recall, 96.8 percent accuracy, and 96.6 percent F1 score in the independent test set. Investigators noted a low calibration error (0.06) was observed with the calibrated model and the feature importance maps suggested that leads III, aVL, V4, V5, and V6 contributed most to the classification performance. Additionally, the predictions made by the algorithm matched the clinical expectations based on biological mechanisms of cardiac involvement in diabetes, according to investigators.
“Machine-learning-based DiaBeats algorithm using ECG signal data accurately predicted diabetes-related classes. This algorithm can help in early detection of diabetes and prediabetes after robust validation in external datasets,” investigators added.