ArticleAnnals of surgical oncology2026
Artificial Intelligence-Driven Three-Dimensional Reconstruction System Reduced Unexpected Procedural Changes in Thoracic Surgery.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence and the Ethical Foundations of Cardiothoracic Surgery: Evidence, Accountability, and the Limits of Delegated Judgment.Medical sciences (Basel, Switzerland) · 2026Review
- ASO Author Reflections: When Age Meets Microvascular Invasion-Uncovering an Ultra-High-Risk Phenotype in Hepatocellular Carcinoma.Annals of surgical oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundAlthough an artificial intelligence-driven three-dimensional reconstruction system (AI-3D) facilitates preoperative planning, its impact on consistency between planned and actual surgical procedures and perioperative outcomes has not been sufficiently investigated.
methodsThis study retrospectively analyzed 1197 patients scheduled for segmentectomy (n = 479) or lobectomy (n = 718) between July 2022 and September 2023. The patients were divided into AI-3D and two-dimensional computed tomography (2D-CT) groups. The primary endpoint was the rate of unexpected procedural changes. The secondary endpoints included intraoperative outcomes (operative time, intraoperative blood loss, and the rate of conversion to open surgery) and postoperative outcomes (second operation, second chest tube, drainage volume, and so forth). Propensity score-matching (PSM) was used to minimize selection bias.
resultsAmong the patients scheduled for segmentectomy, the AI-3D group demonstrated significantly higher consistency between planned and actual procedures than the 2D group (after PSM: 97.3 % vs 80.0 %; P < 0.001). Reason analysis indicated that most changes were attributable to tumor location and/or surgical margin factors. Intra- and postoperative outcomes were almost comparable between the two groups.
conclusionsIn this study, AI-driven 3D reconstruction significantly reduced the rate of unexpected procedural changes in segmentectomy. Although it ensured higher surgical reliability in relation to the preoperative plan, it did not show an advantage in terms of other intra- and postoperative outcomes.
Indexed as
Identifiers
42135548What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.