Artificial Intelligence (AI) – National Institute of …

Early diagnosis of Alzheimers disease (AD) using analysis of brain networks

AD-related neurological degeneration begins long before the appearance of clinical symptoms. Information provided by functional MRI (fMRI) neuroimaging data, which can detect changes in brain tissue during the early phases of AD, holds potential for early detection and treatment. The researchers are combining the ability of fMRI to detect subtle brain changes with the ability of machine learning to analyze multiple brain changes over time. This approach aims to improve early detection of AD, as well as other neurological disorders including schizophrenia, autism, and multiple sclerosis.

NIBIB-funded researchers are building machine learning models to better manage blood glucose levels by using data obtained from wearable sensors. New portable sensing technologies provide continuous measurements that include heart rate, skin conductance, temperature, and body movements. The data will be used to train an artificial intelligence network to help predict changes in blood glucose levels before they occur. Anticipating and preventing blood glucose control problems will enhance patient safety and reduce costly complications.

This project aims to develop an advanced image scanning system with high detection sensitivity and specificity for colon cancers. The researchers will develop deep neural networks that can analyze a wider field on the radiographic images obtained during surgery. The wider scans will include the suspected lesion areas and more surrounding tissue. The neural networks will compare patient images with images of past diagnosed cases. The system is expected to outperform current computer-aided systems in the diagnosis of colorectal lesions. Broad adoption could advance the prevention and early diagnosis of cancer.

Smart, cyber-physically assistive clothing (CPAC) is being developed in an effort to reduce the high prevalence of low back pain. Forces on back muscles and discs that occur during daily tasks are major risk factors for back pain and injury. The researchers are gathering a public data set of more than 500 movements measured from each subject to inform a machine learning algorithm. The information will be used to develop assistive clothing that can detect unsafe conditions and intervene to protect low back health. The long-term vision is to create smart clothing that can monitor lumbar loading; train safe movement patterns; directly assist wearers to reduce incidence of low back pain;and reduce costs related to health care expenses and missed work.

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