ATLAS AI

ATLAS

The ATLAS AI program at Baylor College of Medicine is an interdisciplinary research initiative within the Michael E. DeBakey Department of Surgery. Co-directed by Drs. Ravi Ghanta and Abbas Rana, ATLAS AI was established to accelerate surgical discovery through the integration of clinical expertise, biostatistics, machine learning, and artificial intelligence.

ATLAS AI provides investigators with access to a robust research infrastructure that includes institutional electronic health record data, national clinical and administrative databases, high-performance computing resources, advanced biostatistical support, and software development expertise. By combining these resources, the program enables faculty and trainees to address complex questions in surgical outcomes, healthcare delivery, transplantation, cardiovascular surgery, oncology, trauma, and perioperative care.

Cryotube scanning

Data Banks

EMR Datasets for surgical patients

Big Data:

  • HCUP
  • NIS
  • NRD
  • STS
  • NTDB
Grant writing

Resources

  • High Performance Computers for Machine Learning
  • Software Development
  • Biostaticians
Bobby R. Alford Education Center

Education

ATLAS Team

Ghanta
Ravi Kiran Ghanta
M.D., FACS
Professor and Associate Chief
Rana
Abbas Rana
M.D., FACS
Professor of Surgery
Chatterjee
Subhasis Chatterjee
M.D., FACS
Associate Professor of Surgery

Publications

  1. Zea-Vera R, Ryan CT, Havelka J, Corr SJ, Nguyen TC, Chatterjee S, Wall MJ Jr, Coselli JS, Rosengart TK, Ghanta RK. Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting. Ann Thorac Surg. 2022;114(3):711-719. PMID: 34582751. 
  2. Zea-Vera R, Ryan CT, Navarro SM, Havelka J, Wall MJ Jr, Coselli JS, Rosengart TK, Chatterjee S, Ghanta RK. Development of a Machine Learning Model to Predict Outcomes and Cost After Cardiac Surgery. Ann Thorac Surg. 2023;115(6):1533-1542. PMID: 35917942. 
  3. Ryan CT, Zeng Z, Chatterjee S, Wall MJ, Moon MR, Coselli JS, Rosengart TK, Li M, Ghanta RK. Machine Learning for Dynamic and Early Prediction of Acute Kidney Injury After Cardiac Surgery. J Thorac Cardiovasc Surg. 2023;166(6). doi:10.1016/j.jtcvs.2022.09.045. PMID: 36347651. 
  4. Scioscia JP, Murrieta-Alvarez I, Li S, Xu Z, Zheng G, Uwaeze J, Walther CP, Gray Z, Nordick KV, Braverman V, Shafii AE, Loor G, Hochman-Mendez C, Ghanta RK, Chatterjee S, Frazier OH, Rosengart TK, Liao KK, Mondal NK. Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction. ASAIO J. 2025;71(5):387-394. PMID: 40310715. 
  5. Miles TJ, Guinn MT, Tan X, Qi H, Orozco-Sevilla V, Moon MR, Coselli JS, Rosengart TK, Li M, Chatterjee S, Ghanta RK. Tissue Perfusion Pressure: A Novel Hemodynamic Measure to Assess Risk of Acute Kidney Injury After Cardiac Surgery. J Thorac Cardiovasc Surg. 2026;171(2):455-462.e3. PMID: 40680825. 
  6. Miles TJ, Mendez-Reyes J, Mokhtari AK, Suliburk JW, Wilson CT, Zielinski MD, Ghanta RK. Machine Learning Predicts Mortality and Respiratory Failure in Patients Admitted With Rib Fractures. J Surg Res. 2025;313:153-160. PMID: 40669372. 
  7. Miles TJ, Ghanta RK. Machine Learning in Cardiac Surgery: A Narrative Review. J Thorac Dis. 2024;16(4):2644-2653. PMID: 38738250. 
  8. Pettit RW, Marlatt BB, Corr SJ, Havelka J, Rana A. nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images. Ann Surg Open. 2022;3(2):e155. PMID: 36275876. 
  9. Pettit RW, Marlatt BB, Miles TJ, Uzgoren S, Corr SJ, Shetty A, Havelka J, Rana A. The Utility of Machine Learning for Predicting Donor Discard in Abdominal Transplantation. Clin Transplant. 2023;37(5):e14951. PMID: 36856124. 
  10. Tran BV, Moris D, Markovic D, Zaribafzadeh H, Henao R, et al. Development and Validation of a REcurrent Liver cAncer Prediction ScorE (RELAPSE) Following Liver Transplantation in Patients with Hepatocellular Carcinoma: Analysis of the US Multicenter HCC Transplant Consortium. Liver Transpl. 2023;29(7):683-697. PMID: 37029083.