All Publications

  • Machine Learning Prediction of Clinical Trial Operational Efficiency. The AAPS journal Wu, K., Wu, E., DAndrea, M., Chitale, N., Lim, M., Dabrowski, M., Kantor, K., Rangi, H., Liu, R., Garmhausen, M., Pal, N., Harbron, C., Rizzo, S., Copping, R., Zou, J. 2022; 24 (3): 57


    Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.

    View details for DOI 10.1208/s12248-022-00703-3

    View details for PubMedID 35449371

  • How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nature medicine Wu, E., Wu, K., Daneshjou, R., Ouyang, D., Ho, D. E., Zou, J. 2021

    View details for DOI 10.1038/s41591-021-01312-x

    View details for PubMedID 33820998

  • Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nature medicine Lotter, W., Diab, A. R., Haslam, B., Kim, J. G., Grisot, G., Wu, E., Wu, K., Onieva, J. O., Boyer, Y., Boxerman, J. L., Wang, M., Bandler, M., Vijayaraghavan, G. R., Gregory Sorensen, A. 2021


    Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. 1). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-40% (refs. 2,3). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography6-18, and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. Here we present an annotation-efficient deep learning approach that (1) achieves state-of-the-art performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis (DBT; '3D mammography'), (3) detects cancers in clinically negative prior mammograms of patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new 'maximum suspicion projection' (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.

    View details for DOI 10.1038/s41591-020-01174-9

    View details for PubMedID 33432172