Evaluating Appropriate Role of Artificial Intelligence in Preoperative Abdomen CT Assessment for Living Donor Liver Transplants (LDLT)
Monday 3:20-3:30 PM | SSE09-03 | Room: N230B
PARTICIPANTS:Abhishek Agarwal, MD New Delhi , India (Presenter)
Disclosure: Nothing to Disclose
Suthirth Vaidya, BEng,MENG Bengaluru, India
Disclosure: Stockholder, Predible Health
Digvijay S. Mahra, BEng Bengaluru, India
Disclosure: Stockholder, Predible Health
Adarsh Raj, BEng Bengaluru, India
Disclosure: Stockholder, Predible Health
Krishna Chaitanya Kaluva, BEng,MENG Bangalore, India
Disclosure: Employee, Predible Health
Abhijith Chunduru, MENG Bengaluru, India
Disclosure: Stockholder, Predible Health
Bharat Aggarwal, MBBS, MD New Delhi, India
Disclosure: Nothing to Disclose
PURPOSE
In LDLT, assuring appropriate graft size via evaluation of liver and segmental volumes is a major predictor of safe, successful outcomes. The analysis comprises of two key steps: 1. Segmentation of liver and hepatic vascular structures, and 2. Liver Resection to calculate graft and remnant volumes. Here we aim to study preoperative LDLT assessment using 3 different approaches: A: Fully Manual (Hepatic anatomy is segmented by manual contouring followed by manual resection), B: AI with Manual Resection (Hepatic anatomy is automatically segmented using AI and a radiologist resects manually), and C: Fully Automated (Hepatic anatomy is automatically segmented and resected by AI with no radiologist intervention).
METHOD AND MATERIALS
Our developed AI system comprised of 3 CNN models trained on 324 triphasic contrast-enhanced CTs and validated on 100 CTs from multiple institutions for liver and veins segmentation and middle hepatic vein (MHV) classification. For automated resection (C), we sample points from the MHV and IVC to draw a resection plane and return the graft and remnant volumes. 100 retrospective abdomen CT scans with preoperative analysis done were extracted from a large tertiary hospital. 6 studies were excluded due to incomplete information. On the remaining 94 CTs, the graft and remnant volumes were generated for A, B, and C. Intraoperative surgical weights were collected for comparison as ground truth.
RESULTS
We measured the variance of graft volume for A, B, and C against intraoperative surgical weight. B has the least overall variance of 9.14%, followed by C (9.32%) and A (10.62%) on 94 cases. A close correlation (variance < 5%) with the weight was seen in 40 cases using C as compared to 39 cases using B and 32 cases using A. Fig 1 shows the boxplot of the variance of A, B, and C.
CONCLUSION
Amongst the 3 approaches for LDLT analysis, AI with Manual Resection (B) and Fully Automated (C) give the best results, with B displaying the least overall variance.
CLINICAL RELEVANCE/APPLICATION
While AI can automate routine mundane tasks such as hepatic structure segmentation, an AI system coupled with expert intervention is poised to deliver better outcomes in Liver Transplant Planning.
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