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Blog Post number 1
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publications
The Artificial Intelligence Journal Club (# RADAIJC): a multi-institutional resident-driven web-based educational initiative
Published in Academic radiology, 2020
Recommended citation: Balthazar, P., Tajmir, S. H., Ortiz, D. A., Herse, C. C., Shea, L. A., Seals, K. F., ... & Gichoya, J. W. (2020). The Artificial Intelligence Journal Club (# RADAIJC): a multi-institutional resident-driven web-based educational initiative. Academic radiology, 27(1), 136-139.
Just in time radiology decision support using real-time data feeds
Published in Journal of digital imaging, 2020
Recommended citation: Burns, J. L., Hasting, D., Gichoya, J. W., McKibben, B., Shea, L., & Frank, M. (2020). Just in time radiology decision support using real-time data feeds. Journal of digital imaging, 33(1), 137-142.
How might AI and chest imaging help unravel COVID-19’s mysteries?
Published in Radiology: Artificial Intelligence, 2020
Recommended citation: Kundu, S., Elhalawani, H., Gichoya, J. W., & Kahn Jr, C. E. (2020). How might AI and chest imaging help unravel COVID-19’s mysteries?.
Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks.
Published in KDH@ ECAI, 2020
Recommended citation: Bhimireddy, A. R., Sinha, P., Oluwalade, B., Gichoya, J. W., & Purkayastha, S. (2020, August). Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks. In KDH@ ECAI (pp. 125-130).
Current clinical applications of artificial intelligence in radiology and their best supporting evidence
Published in Journal of the American College of Radiology, 2020
Recommended citation: Tariq, A., Purkayastha, S., Padmanaban, G. P., Krupinski, E., Trivedi, H., Banerjee, I., & Gichoya, J. W. (2020). Current clinical applications of artificial intelligence in radiology and their best supporting evidence. Journal of the American College of Radiology, 17(11), 1371-1381.
The state of radiology AI: considerations for purchase decisions and current market offerings
Published in Radiology: Artificial Intelligence, 2020
Recommended citation: Tadavarthi, Y., Vey, B., Krupinski, E., Prater, A., Gichoya, J., Safdar, N., & Trivedi, H. (2020). The state of radiology AI: considerations for purchase decisions and current market offerings. Radiology: Artificial Intelligence, 2(6), e200004.
Failures hiding in success for artificial intelligence in radiology
Published in Journal of the American College of Radiology, 2021
Recommended citation: Purkayastha, S., Trivedi, H., & Gichoya, J. W. (2021). Failures hiding in success for artificial intelligence in radiology. Journal of the American College of Radiology, 18(3), 517-519.
Uterine Artery Embolization for Secondary Postpartum Hemorrhage
Published in Techniques in Vascular and Interventional Radiology, 2021
Recommended citation: Loya, M. F., Garcia-Reyes, K., Gichoya, J., & Newsome, J. (2021). Uterine Artery Embolization for Secondary Postpartum Hemorrhage. Techniques in Vascular and Interventional Radiology, 24(1), 100728.
Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study
Published in The Lancet Digital Health, 2021
Recommended citation: Sarkar, R., Martin, C., Mattie, H., Gichoya, J. W., Stone, D. J., & Celi, L. A. (2021). Performance of intensive care unit severity scoring systems across different ethnicities in the USA: a retrospective observational study. The Lancet Digital Health, 3(4), e241-e249.
Beyond the AJR: “An algorithmic approach to reducing unexplained pain disparities in underserved populations”
Published in American Journal of Roentgenology, 2021
Recommended citation: Gichoya, J. W., & Celi, L. A. (2021). Beyond the AJR:“An algorithmic approach to reducing unexplained pain disparities in underserved populations”. American Journal of Roentgenology.
Patient-specific COVID-19 resource utilization prediction using fusion AI model
Published in NPJ digital medicine, 2021
Recommended citation: Tariq, A., Celi, L. A., Newsome, J. M., Purkayastha, S., Bhatia, N. K., Trivedi, H., ... & Banerjee, I. (2021). Patient-specific COVID-19 resource utilization prediction using fusion AI model. NPJ digital medicine, 4(1), 1-9.
Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV
Published in 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021
Recommended citation: Kodela, S., Pinnamraju, J., Gichoya, J. W., & Purkayastha, S. (2021, June). Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 225-230). IEEE.
Village mentoring and hive learning: The MIT Critical Data experience
Published in iScience, 2021
Recommended citation: Cosgriff, C. V., Charpignon, M., Moukheiber, D., Lough, M. E., Gichoya, J., Stone, D. J., & Celi, L. A. (2021). Village mentoring and hive learning: The MIT Critical Data experience. iScience, 24(6).
Managing Postembolization Syndrome–Related Pain after Uterine Fibroid Embolization
Published in Seminars in Interventional Radiology, 2021
Recommended citation: Chan, P., Garcia-Reyes, K., Cronan, J., Newsome, J., Bercu, Z., Majdalany, B. S., ... & Kokabi, N. (2021, August). Managing Postembolization Syndrome–Related Pain after Uterine Fibroid Embolization. In Seminars in Interventional Radiology (Vol. 38, No. 03, pp. 382-387). Thieme Medical Publishers, Inc..
A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images
Published in Journal of Digital Imaging, 2021
Recommended citation: Kathiravelu, P., Sharma, P., Sharma, A., Banerjee, I., Trivedi, H., Purkayastha, S., ... & Gichoya, J. W. (2021). A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images. Journal of Digital Imaging, 34(4), 1005-1013.
SCU‐Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms
Published in Medical Physics, 2021
Recommended citation: Guo, X., O'Neill, W. C., Vey, B., Yang, T. C., Kim, T. J., Ghassemi, M., ... & Banerjee, I. (2021). SCU‐Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms. Medical Physics, 48(10), 5851-5861.
PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor
Published in Data in Brief, 2021
Recommended citation: Mahajan, Y., Bhimireddy, A., Abid, A., Gichoya, J. W., & Purkayastha, S. (2021). PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor. Data in Brief, 38, 107287.
Optimizing Medical Image Classification Models for Edge Devices
Published in International Symposium on Distributed Computing and Artificial Intelligence, 2021
Recommended citation: Abid, A., Sinha, P., Harpale, A., Gichoya, J., & Purkayastha, S. (2021, October). Optimizing Medical Image Classification Models for Edge Devices. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 77-87). Springer, Cham.
Query bot for retrieving patients’ clinical history: a COVID-19 use-case
Published in Journal of Biomedical Informatics, 2021
Recommended citation: Wang, Y., Tariq, A., Khan, F., Gichoya, J. W., Trivedi, H., & Banerjee, I. (2021). Query bot for retrieving patients’ clinical history: a COVID-19 use-case. Journal of Biomedical Informatics, 123, 103918.