E-199 AI-driven analytics platform for advanced surgical or monitoring in neurointervention: World’s first feasibility study
Notice bibliographique
Résumé
Introduction/Purpose Safety in the Operating Room (OR) has improved in recent years with technical advancements, from modernization of medical equipment to personnel training and implementation of standard operating procedures. While the operator and surgical team have been trained with communication and teamwork skills that support situational awareness of errors in the OR, adverse events and workflows have nevertheless gone unnoticed. The OR Black Box™ is a system that tracks activities in the OR with cameras, sensors, and audio recording equipment from which data can be analyzed by deep learning technology. There is opportunity to proactively evaluate safety and efficiency in the interventional radiology workflow for endovascular interventions. The purpose of this study is to assess the angiosuite environment and identify potential areas of improvement in the neurointerventional radiology suite to support the clinical team in improving patient care. Materials and Methods The OR Black Box™ system was installed in two dedicated neurointervention procedure suites: Philips Azurion and Siemens ARTIS icono. Audiovisual data was securely transmitted from each OR to the Surgical Safety Technologies Servers at the hospital data center. Analysis of the technical and non-technical performance, as well as environmental distractions during the procedure was conducted using a machine learning algorithm. Technical performance was assessed with the Generic Error Rating Tool (GERT) and Objective Structured Assessment of Technical Skills (OSATS). The non-technical performance was assessed by the Scrub Practitioners’ intra-operative non-technical skills (SPINTS - SPLINTS) and Non-Technical Skills for Surgeons (NOTSS). Results We present data on the initial series of cases performed in our two angiosuites, including procedures for aneurysms, acute ischemic stroke, and arteriovenous embolization. The workflow performance will be analyzed to identify organizational and patient-related disruptions, as well communication gaps that limit team cohesion for optimal patient care. Furthermore, the results will demonstrate the gaps in the surgical team’s awareness of adverse events that could be addressed by improving the physical organization of the operating room and communication method between the neurointerventional, nursing, technologist, and anesthesiology teams. As such, the output of the OR Blackbox™ will reveal the root cause of adverse outcomes that can be prevented through individualized team training interventions for each of the two procedure suites. Results will be discussed in comparison to OR Blackbox™ in other areas of surgery, which would encourage adaptations of quality improvement practices between disciplines. Conclusion The current study will provide insight on the implementation of the OR Black Box™ in the first neurointerventional radiology suites in the world. The preliminary results will guide implementation of improved protocols to optimize OR safety and increase efficiency of the interventional neurosugery workflow. We believe that deep learning technology will drive the future of neurointerventional procedures, and neurointerventional safety is no exception. While new understandings could disrupt standard practices, the ultimate safety and efficiency achieved will optimize patient outcomes. Disclosures V. Chan: None. N. Cancelliere: None. V. Mendes Pereira: None.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».