ArticleScientific reports2021
Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- State-of-the-Art of Non-Radiative, Non-Visual Spine Sensing with a Focus on Sensing Forces, Vibrations and Bioelectrical Properties: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Feasibility of Drill-Tip Position Estimation During Cortical Bone Drilling Using Force and Torque Signals.Sensors (Basel, Switzerland) · 2026Article
- Ex Vivo Differentiation of Cartilage Degeneration Severity and Subchondral Bone Using Vibroacoustic Signals from Instrument-Tissue Interaction.Sensors (Basel, Switzerland) · 2026Article
- Sound source localization for spatial mapping of surgical actions in dynamic scenes.International journal of computer assisted radiology and surgery · 2026Article
- Cobotic drilling assistant for orthopedic surgery using Gaussian process-based breakthrough detection.International journal of computer assisted radiology and surgery · 2026Article
- Acoustic cues into a surgeon-assist physical AI for detecting bone penetration during spinal surgery.Scientific reports · 2026Article
- Force-sensing drill-through detection and automatic stopping in a porcine distal humerus model simulating pediatric supracondylar pinning.Frontiers in pediatrics · 2026Article
- Artificial Intelligence in Spine Surgery: Imaging-Based Applications for Diagnosis and Surgical Techniques.Current reviews in musculoskeletal medicine · 2025Review
- Review
- Exploratory analysis and framework for tissue classification based on vibroacoustic signals from needle-tissue interaction.International journal of computer assisted radiology and surgery · 2025Article
- From error to prevention of wrong-level spine surgery: a review.Patient safety in surgery · 2025Review
- Computer vision-guided rapid and precise automated cranial microsurgeries in mice.Science advances · 2025Article
- A new sensing paradigm for the vibroacoustic detection of pedicle screw loosening.Medical & biological engineering & computing · 2025Article
- High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.BMC medical ethics · 2025Review
- The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis.International journal of computer assisted radiology and surgery · 2024Article
- Artificial Intelligence in Surgery: A Systematic Review of Use and Validation.Journal of clinical medicine · 2024Review
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Using acoustic feedback and analyses for removal of bone cement during revision knee replacement surgeries.International journal of computer assisted radiology and surgery · 2023Article
- Verification, Evaluation, and Validation: Which, How & Why, in Medical Augmented Reality System Design.Journal of imaging · 2023Article
- Surgical training fit for the future: the need for a change.Postgraduate medical journal · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, we developed and validated a computer method capable of robustly detecting drill breakthrough events and show the potential of deep learning-based acoustic sensing for surgical error prevention. Bone drilling is an essential part of orthopedic surgery and has a high risk of injuring vital structures when over-drilling into adjacent soft tissue. We acquired a dataset consisting of structure-borne audio recordings of drill breakthrough sequences with custom piezo contact microphones in an experimental setup using six human cadaveric hip specimens. In the following step, we developed a deep learning-based method for the automated detection of drill breakthrough events in a fast and accurate fashion. We evaluated the proposed network regarding breakthrough detection sensitivity and latency. The best performing variant yields a sensitivity of [Formula: see text]% for drill breakthrough detection in a total execution time of 139.29[Formula: see text]. The validation and performance evaluation of our solution demonstrates promising results for surgical error prevention by automated acoustic-based drill breakthrough detection in a realistic experiment while being multiple times faster than a surgeon's reaction time. Furthermore, our proposed method represents an important step for the translation of acoustic-based breakthrough detection towards surgical use.
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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.