ArticleScientific reports2022
Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Neurosurgical skills analysis by machine learning models: systematic review.Neurosurgical review · 2023Pooled it
- Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Perioperative blood loss in robotic urologic surgery: a retrospective evaluation of estimation methods.Journal of robotic surgery · 2026Article
- Article
- Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Spatial-temporal attention for video-based assessment of intraoperative surgical skill.Scientific reports · 2024Article
- Artificial intelligence: revolutionizing robotic surgery: review.Annals of medicine and surgery (2012) · 2024Review
- Advancements in Skull Base Surgery: Navigating Complex Challenges with Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Simulated outcomes for durotomy repair in minimally invasive spine surgery.Scientific data · 2024Article
- Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.Advances in experimental medicine and biology · 2024Review
- Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.Operative neurosurgery (Hagerstown, Md.) · 2023Article
- Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2023Review
- Editorial: Machine learning in data analysis for stroke/endovascular therapy.Frontiers in neurology · 2023Article
- Artificial Intelligence Technologies in the Microsurgical Operating Room (Review).Sovremennye tekhnologii v meditsine · 2023Review
- Computer-vision based analysis of the neurosurgical scene - A systematic review.Brain & spine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
Abstract
Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset of these events, surgeons do not know how much blood will be lost or whether they will successfully control the hemorrhage (achieve hemostasis). We evaluate the ability of a deep learning neural network (DNN) to predict hemostasis control ability using the first minute of surgical video and compare model performance with human experts viewing the same video. The publicly available SOCAL dataset contains 147 videos of attending and resident surgeons managing hemorrhage in a validated, high-fidelity cadaveric simulator. Videos are labeled with outcome and blood loss (mL). The first minute of 20 videos was shown to four, blinded, fellowship trained skull-base neurosurgery instructors, and to SOCALNet (a DNN trained on SOCAL videos). SOCALNet architecture included a convolutional network (ResNet) identifying spatial features and a recurrent network identifying temporal features (LSTM). Experts independently assessed surgeon skill, predicted outcome and blood loss (mL). Outcome and blood loss predictions were compared with SOCALNet. Expert inter-rater reliability was 0.95. Experts correctly predicted 14/20 trials (Sensitivity: 82%, Specificity: 55%, Positive Predictive Value (PPV): 69%, Negative Predictive Value (NPV): 71%). SOCALNet correctly predicted 17/20 trials (Sensitivity 100%, Specificity 66%, PPV 79%, NPV 100%) and correctly identified all successful attempts. Expert predictions of the highest and lowest skill surgeons and expert predictions reported with maximum confidence were more accurate. Experts systematically underestimated blood loss (mean error - 131 mL, RMSE 350 mL, R
Indexed as
Identifiers
What 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.