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All Publications

  • Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks Shenoy, V. N., Foster, E., Aalami, L., Majeed, B., Aalami, O., Zheng, H., Callejas, Z., Griol, D., Wang, H., Hu, Schmidt, H., Baumbach, J., Dickerson, J., Zhang, L. IEEE. 2018: 1017–21

    View details for Web of Science ID 000458654000179

  • Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition. AMIA ... Annual Symposium proceedings. AMIA Symposium Shenoy, V. N., Aalami, O. O. 2017; 2017: 1564-1570

    Abstract

    Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking a patient's health. Trends in these measurements can accurately track diabetes, cardiovascular issues, and assist medication management for patients. Currently, patients record their results and date of measurement in a physical notebook. It may be weeks before a doctor sees a patient's records and can assess the health of the patient. With a predicted 6.8 billion smartphones in the world by 20221, health monitoring platforms, such as Apple's HealthKit2, can be leveraged to provide the right care at the right time. This research presents a mobile application that enables users to capture medical monitor data and send it to their doctor swiftly. A key contribution of this paper is a robust engine that can recognize digits from medical monitors with an accuracy of 98.2%.

    View details for PubMedID 29854226

    View details for PubMedCentralID PMC5977613