Exploring Anatomical Variations of Abdominal Arteries Through Computed Tomography: Classification, Prevalence and Implications
Bibliographic record
Abstract
BACKGROUND AND AIMS: Variations in the branches of the abdominal aorta are relatively prevalent and can impact certain surgeries. The accurate identification and differentiation of these variations pre- and intraoperatively are crucial to avoid negative clinical sequelae. This study aimed to investigate the prevalence of variations in some branches of the abdominal aorta and to identify the most frequent variants as well as any rare variants not previously classified in the existing classification systems. The study's findings may help improve the understanding and management of these variations. MATERIALS AND METHODS: This retrospective study was conducted at the Department of Radiology at An-Najah National University Hospital (NNUH) and included 550 abdominal computed tomography (CT) angiographic scans for patients (51.5% males, 48.5% females) performed between January 2017 and January 2023. RESULTS: Variations were most common in the hepatic arteries (34.7%), followed by the renal arteries (31.3%). Variations in the celiac trunk were the least frequent (9.8%). The gastro-splenic trunk (type V) was the most common celiac trunk variant. The most common hepatic artery variant was the replacement of the right hepatic artery (type III). Accessory renal arteries were more frequent on the left side and among males (P = 0.01). The celiac trunk variations had a significant association with the hepatic artery variations (P = 0.001) and the renal artery variations (P = 0.011), respectively. CONCLUSION: There is a high prevalence of anatomical variations in the described vessels, and it matches the results in the reported literature. Our findings also suggest the possible coexistence of variants. We have also encountered rare variants, especially in the hepatic arterial system. Some of the hepatic arterial system variants are not included in the older classification systems, calling for an extension of the old systems (Michel's and Hiatt classification systems) or replacement with the newer (CRL or EX-CRL classification systems) to account for rare variants not previously classified. Radiologists and surgeons should be proficient in identifying and differentiating these variations to take precautions and actions for each variant individually.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".