Use of augmented reality with image fusion to facilitate surgical stoma creation: an IDEAL stage 2A case series
Bibliographic record
Abstract
Introduction Augmented reality (AR) has been increasingly applied to surgical procedures in fixed anatomical organs like brain, bones, aorta and kidneys, enabling image-guided precision, but sparingly to mobile organs such as the intestines. We report our initial experience with AR-guided intestinal stoma creation using an “image-guided” minimally invasive approach. Methods Adult patients requiring elective or urgent stoma creation for colonic decompression or diversion were included. Patient-specific 3D reconstructions of the relevant portion of the GI tract and reference organs (skin, bones, vessels) from a preoperative CT were co-registered intraoperatively via a head-mounted Augmented reality device (HoloLens2) onto the patient’s body using surface landmarks visible such as the umbilicus, bones, and prior surgical scars. A trajectory to the target bowel loop based on AR was marked on the skin, and stoma creation was performed at this site. Targeting of the correct bowel loop was confirmed with intraoperation fluoroscopy using intralumenal contrast injection. Technical success was defined as completion at the targeted site without open surgery. Results Fourteen patients underwent AR-guided stoma creation (9 colostomies, 5 ileostomies). Indications were bowel obstruction (n = 6), fistula (n = 5), anastomotic leak (n = 1), perforation (n = 1) and gastrointestinal bleeding (n = 1). Median age was 76 years, median BMI 23.8 kg/m 2 . The median (range) number of prior abdominal surgeries was 2 (0–11). The median operative time was 131 min (interquartile range [IQR]: 96–143). The approach was either cut down directly over the stoma site (n = 11) or laparoscopic assisted (n = 3). AR permitted precise identification of the bowel loop required for stoma creation in all cases and help to avoid need for standard open surgery. Median postoperative stay was 7 days (interquartile range: 3–10). No Clavien-Dindo grade III or IV complications, reoperations, or unplanned readmissions were observed. Two postoperative deaths occurred in ASA 4 patients, both due to the underlying malignancy and multiorgan failure preoperatively, unrelated to the surgical procedure. Conclusion This early experience suggests AR methods may identify and target a loop of bowel, play a useful role in intestinal stoma creation, with potential to avoid need for laparoscopy or extensive open surgery. Further clinical application and refinement are warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".