MIGHT - AI Medical Diagnostics Reliability Method

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MIGHT - AI Medical Diagnostics Reliability Method

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Two studies led by Johns Hopkins Kimmel Cancer Center, Ludwig Center, and Johns Hopkins Whiting School of Engineering researchers report on a powerful new method that significantly improves the reliability and accuracy of artificial intelligence (AI) for many applications. As an example, they apply the new method to early cancer detection from blood samples, known as liquid biopsy. One study reports on the development of MIGHT (Multidimensional Informed Generalized Hypothesis Testing), an AI method that the researchers created to meet the high level of confidence needed for AI tools used in clinical decision making. To illustrate the benefits of MIGHT, they used it to develop a test for early cancer detection using circulating cell-free DNA (ccfDNA)—fragments of DNA circulating in the blood. A companion study found that ccfDNA fragmentation patterns used to detect cancer also appear in patients with autoimmune and vascular diseases. To develop a test with high sensitivity for cancer but reduced false-positive results, MIGHT was expanded to incorporate data from autoimmune and vascular diseases obtained from colleagues at Johns Hopkins and other institutions. MIGHT uses tens of thousands of decision trees to fine-tune itself with real data and check accuracy across different data subsets. The algorithm is particularly effective for analyzing biomedical datasets with many variables but relatively few patient samples, a common challenge where traditional AI models often struggle. In testing with patient data, MIGHT consistently outperformed other AI methods in both sensitivity and consistency. When applied to blood samples from 1,000 individuals—352 patients with advanced cancers and 648 individuals without cancer—the algorithm achieved 72% sensitivity at 98% specificity using aneuploidy-based features. "MIGHT gives us a powerful way to measure uncertainty and increase reliability, especially in situations where sample sizes are limited but data complexity is high," said Joshua Vogelstein, PhD, associate professor of biomedical engineering and lead investigator. The research was published in the Proceedings of the National Academy of Sciences. The two studies illustrate how MIGHT addresses key challenges in clinical AI implementation by improving accuracy and reducing false positives in liquid biopsy testing.