Collaborative AI-for-Health Projects

We have collaborated with various colleagues in the UC Davis Health - School of Medicine on applying Internet-of-Things (IoTs), signal processing, and machine learning/artificial intelligence (ML/AI) techniques to critical care risk assessments, disease detection (ICCare), and patient management (EPACC), neuropathology image analysis (AI Pathology), and Autism screening (ASDeep). Our team also has success adapting the AI/ML workflow developed in the ICCare and EPACC projects to other medical sub-domains, including the two projects highlighted below.

AIoT-based Critical Congenital Heart Disease (CCHD) Screening

Lead Domain Expert: Dr. Heather Siefkes, UC Davis Health-Pediatrics

Overview: Critical congenital heart disease (CCHD) is a common group of neonatal life-threatening defects that must be promptly diagnosed to minimize morbidity and mortality. However, despite current screening practices involving oxygen-saturation analysis, timely diagnosis is missed in approximately 900 infants with CCHD annually in the United States (US) and can benefit from increased data processing capabilities. Adding non-invasive perfusion measurements to oxygen-saturation data can improve timeliness and fidelity of CCHD diagnostics. However, real-time monitoring and interpretation of non-invasive perfusion data is currently limited. In this project, we created a hardware and software architecture utilizing a Pi-top for collecting, visualizing, and storing dual oxygen-saturation, perfusion indices, and photoplethysmography data. From these data, we are able to extract non-invasive perfusion features such as perfusion index, radiofemoral delay, and slope of systolic rise or diastolic fall. We use the collected data set to develop proof-of-concept ML algorithm that combines pulse oximetry features to improve detection of CCHD with little impact on false positive rate.

Publications

  • L. Cerny Oliveira, Z. Lai, H. Siefkes, and C-N. Chuah, "Generalizable Semi-supervised Learning Strategies for Multiple Learning Tasks using 1-D Biomedical Signals," NeurIPS Workshop on Learning from Time Series for Health, Dec 2022.
  • L, Cerny Oliveira, Z. Lai, W. Geng, H. Siefkes and C-N. Chuah, "A Machine Learning Driven Pipeline for Automated Photoplethysmogram Signal Artifact Detection," IEEE/ACM 1st Workshop on Artificial Intelligence and Internet of Things for Digital Health (AIIoT4DH), co-located with IEEE/ACM Conference on Connected Health Applications, Systems, and Engineering Technologies (CHASE), Dec 16-18, 2021.
  • Z. Lai, P. Vadlaputi, D. J. Tancredi, M. Garg, R. I. Koppel, M. Goodman, W. Hogan, N. Cresalia, S. Juergensen, E. Manalo, S. Lashminrusimha, C-N. Chuah, and H. Siefkes, "Enhanced Critical Congenital Cardiac Disease Screening by Combining Interpretable Machine Learning Algorithms," 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Oct 31-Nov 4, 2021.
  • K. Doshi, G. Rehm, P. Vadlaputi, Z. Lai, S. Lakshminrusimha, C-N. Chuah, and H. M Siefkes, "A Novel System to Collect Dual Pulse Oximetry Data for Critical Congenital Heart Disease Screening Research," Journal of Clinical and Translational Science, pp. 1-25, October 2020. [DOI: 10.1017/cts.2020.550]
Abstracts
  • Z. Lai, P. Vadlaputi, D. Tancredi, M. Garg, R. Koppel, M. Goodman, M, W. Hogan, N. Cresalia, S. Juergensen, E. Manalo, S. Lakshminrusimha, C. Chuah, and H. Siefkes, "Machine Learning Algorithm Combining Pulse Oximetry Features for Critical Congenital Heart Disease Screening," Pediatric Academic Society, May 2021.
  • P. Vadlaputi, Z. Lai, M. Garg, R. Koppel, M. Goodman, M, W. Hogan, N. Cresalia, S. Juergensen, E. Manalo, C. Chuah, S. Lakshminrusimha, and H. Siefkes, "Simple Automation Reduces False Positive Rate in Perfusion Index Critical Congenital Heart Disease (CCHD) Screening," Eastern Society for Pediatric Research Annual Meeting, March 2021.
  • H. Siefkes, K. Doshi, G. Rehm, P. Vadlaputi, E. Manalo, W. J. Hogan, M. Garg, R. Koppel, D. Trancedi, S. Lakshminrusimha, C-N. Chuah, "Improving Critical Congenital Heart Disease Screening with Addition of Perfusion Measurements," Translational Science , 2020.

Funding

    This collaborative work is supported by UC Davis AI-for-Health Seed Grant, UCD VEnture Catalyst DIAL Grant, and NIH grant #R21HD099239-01.

ML-Driven Detection of Hypertension Disorder of Pregnancy using ECG data

Lead Domain Expert: Dr. Lihong Mo, UC Davis Health-Department of Obstetrics and Gynecology

Preeclampsia, or more broadly Hypertension Disorder of Pregnancy (HDP), is one of the leading causes of maternal and perinatal mortality worldwide. HDP, impacting 10% of pregnancies, serves as an early indicator of cardiovascular disease (CVD). Racial and ethnic minority groups in the United States are disproportionately affected by HDP and associated morbidity. Our earlier work quantitatively demonstrats that incorporation of zip codes related Area Deprivation Index (ADI) and social determinants of health (SDOH) variables can improve the prediction performance of ML-classifer for postpartum readmissions due to preeclampsia. In other words, socially disadvantaged patients are more likely to be readmitted for worsening preeclampsia in the postpartum period.

The physiological stress of pregnancy on the cardiovascular system is reflected in ECGs, offering a potential tool for early risk assessment. In this project, we investigates whether ECGs can accurately predict pregnancy, trimesters, HDP subtypes, and long-term CVD outcomes.

Publications

Abstracts
  • L. Mo, S. Rai Sharma, N. Sandhu, H. Hedriana, Z. Chithiwala, A. Curtin, and C-N. Chuah, "Social Determinants of Health Improves Prediction for Postpartum Readmissions Due to Preeclampsia," Society of Maternal Fetal Medicine, vol. 23, Issue 1, Supplement S149, January 2024.
  • L. Mo, S. M. Joshi, S. gupta, V. J. Pae, I. Uche, H. R. Shaik, C-N. Chuah, P. Strong, U. Srivasta, I. Ebong, H. Hedriana, E. Waetjen, "Machine Learning-based Pregnancy and Pregnancy Trimesters Prediction Using Electrocardiogram," Society for Maternal-Fetal Medicine (SMFM) Pregnancy Meeting, Jan 27- Feb 1, 2025.

Funding

    This collaborative work is supported by UC Davis Center for Diagnostic Innovation (CDxI) Pilot Grant and School of Medicine (SOM) Data Science Innovation grant 2024-25.