Real three‐dimensional approach vs two‐dimensional camera with and without real‐time near‐infrared imaging with indocyanine green for detection of endometriosis: A case‐control study
Bibliographic record
Abstract
INTRODUCTION: The complete surgical removal of endometriosis lesions is not always feasible because some implants may be very small or hidden. The use of intraoperative near-infrared radiation (NIR) imaging after intravenous injection of indocyanine green (ICG) coupled with robotic technical advances, including three-dimensional (3D) and high-resolution vision, might improve detection rates. MATERIAL AND METHODS: This is a retrospective, multicenter case-control study (Canadian Task Force classification II-2) on medical records of women with endometriosis who underwent surgery at the Catholic University of Rome (Controls) and the University of Bologna (Cases) between January 2016 and March 2018. Surgical and post-surgical data from the procedures were collected. We compared the visual detection rate of endometriotic lesions using near-infrared radiation imaging after intravenous injection of indocyanine green (NIR-ICG) in Real 3D (Cases) with the 2D Camera approach (Controls) in symptomatic women with pelvic endometriosis. RESULTS: Twenty cases were matched as closely as possible with 27 controls. The numbers of suspected lesions identified both with the white light and the NIR-ICG approach were 116 and 70 in the Controls (2D) and Cases (3D), respectively. Among them, 16 of 116 controls (13.8%) and 12 of 70 cases (17.1%) were identified using only NIR-ICG imaging and collected as occult lesions (P = .536). The overall NIR-ICG lesion identification showed a positive predictive value of 97.8%, negative predictive value of 82.3%, sensitivity of 82.0%, and specificity of 97.9% for the Control group, and a positive predictive value of 100%, negative predictive value of 97.1%, sensitivity of 97.1%, and specificity of 100% for the Case group, confirming that NIR-ICG imaging is a good diagnostic and screening test (P = .643 and P = .791, according to the Cohen κ tests, respectively for the laparoscopic and robotic groups). CONCLUSIONS: The few differences observed did not seem to be clinically relevant, making the 2 procedures comparable in terms of the ability to visually detect endometriotic lesions. Further prospective trials are needed to confirm our results.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".