ArticleNPJ digital medicine2025
Systematic review of machine learning applications using nonoptical motion tracking in surgery.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Review
- Cross-Stream and Cross-Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand Kinematics.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Flexible IMU Sensor Array For 3D Colonoscope Shape Reconstruction and AI-Based Loop Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence-enhanced microsurgical training: a systematic review.NPJ digital medicine · 2026Article
- Towards accurate and interpretable competency-based assessment: enhancing clinical competency assessment through multimodal AI and anomaly detection.NPJ digital medicine · 2026Article
- Explainable machine learning using EMG and accelerometer sensor data quantifies surgical skill and identifies biomarkers of expertise.Scientific reports · 2025Article
- Artificial intelligence in microsurgery and supermicrosurgery training within plastic surgery: A systematic review.JPRAS open · 2025Review
- Artificial Intelligence in Healthcare: A Narrative Review of Recent Clinical Applications, Implementation Strategies, and Challenges.Journal of healthcare leadership · 2025Review
- Effectiveness of a 4-day intensive course for neurosurgeons in error reduction in microvascular anastomoses.Brain & spine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. Skill assessment was the primary objective (38%). NOMTS used included internal device kinematics (56%), electromagnetic (17%), inertial (15%), mechanical (11%), and electromyography (1%) sensors. Surgical settings were robotic (60%), laparoscopic (18%), open (16%), and others (6%). Procedures focused on bench-top tasks (67%), clinical models (17%), clinical simulations (9%), and non-clinical simulations (7%). Over 90% accuracy was achieved in 36% of studies. Literature shows NOMTS and ML can enhance surgical precision, assessment, and training. Future research should advance ML in surgical environments, ensure model interpretability and reproducibility, and use larger datasets for accurate evaluation.
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.