78 Automated ML pipeline for analysis of paraesophageal hernias
Sharon A Lee, Rakesh Sharma, Walter R Witschey, Maria S Altieri, Victoria M Gershuni, David A Mankoff, Daniel A Hashimoto · 2026
Read 78 Automated ML pipeline for analysis of paraesophageal hernias by Sharon A Lee, Rakesh Sharma, Walter R Witschey, Maria S Altieri, Victoria M Gershuni, David A Mankoff, Daniel A Hashimoto (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13173096.txt.
Description
A paraesophageal hernia lets part of the stomach slip upward beside the food pipe, and doctors must judge who needs repair before an emergency strikes. In a conference abstract, a seven-author team describes a planned study at Penn Medicine. They will build a machine learning pipeline that reads CT scans of such patients and extracts measures like the size of the hernia opening and the volume of stomach that has moved.
The plan links those image measures and clinical data to two outcomes: who goes on to elective repair, and who develops a twisted stomach or blockage. The pipeline aims to segment organs and pull features automatically, using current medical imaging models. The team expects larger hernias to track with both outcomes. For patients now watched without surgery, such a tool could sharpen the call on when to operate.
Catalog record
| CREATOR | Sharon A Lee, Rakesh Sharma, Walter R Witschey, Maria S Altieri, Victoria M Gershuni, David A Mankoff, Daniel A Hashimoto |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |