Advancing neurosurgery with image-guided robotics
Notice bibliographique
Résumé
Robotic systems are being introduced into surgery to extend human ability. NeuroArm represents a potential change in the way surgery is performed; this is the first image-guided, MR-compatible surgical robot capable of both microsurgery and stereotaxy. This paper presents the first surgical application of neuroArm in an investigation of microsurgical performance, navigation accuracy, and Phase I clinical studies. To evaluate microsurgical performance, 2 surgeons performed microsurgery (splenectomy, bilateral nephrectomy, and thymectomy) in a rodent model using neuroArm and conventional techniques. Two senior residents served as controls, using the conventional technique only (8 rats were used in each of the 3 treatment groups; the 2 surgeons each treated 4 rats from each group). Total surgery time, blood loss, thermal injury, vascular injury, and animal death due to surgical error were recorded and converted to an overall performance score. All values are reported as the mean +/- SEM when normally distributed and as the median and interquartile range when not. Surgeons were slower using neuroArm (1047 +/- 69 seconds) than with conventional microsurgical techniques (814 +/- 54 seconds; p = 0.019), but overall performance was equal (neuroArm: 1110 +/- 82 seconds; microsurgery: 1075 +/- 136 seconds; p = 0.825). Using microsurgery, the surgeons had overall performance scores equal to those of the control resident surgeons (p = 0.141). To evaluate navigation accuracy, the localization error of neuroArm was compared with an established system. Nanoparticles were implanted at predetermined bilateral targets in a cadaveric model (4 specimens) using image guidance. The mean localization error of neuroArm (4.35 +/- 1.68 mm) proved equal to that of the conventional navigation system (10.4 +/- 2.79 mm; p = 0.104). Using the conventional system, the surgeon was forced to retract the biopsy tool to correct the angle of entry in 2 of 4 trials. To evaluate Phase I clinical integration, the role of neuroArm was progressively increased in 5 neurosurgical procedures. The impacts of neuroArm on operating room (OR) staff, hardware, software, and registration system performance were evaluated. NeuroArm was well received by OR staff and progressively integrated into patient cases, starting with draping in Case 1. In Case 2 and all subsequent cases, the robot was registered. It was used for tumor resection in Cases 3-5. Three incidents involving restrictive cable length, constrictive draping, and reregistration failure were resolved. In Case 5, the neuroArm safety system successfully mitigated a hardware failure. NeuroArm performs as well and as accurately as conventional techniques, with demonstrated safety technology. Clinical integration was well received by OR staff, and successful tumor resection validates the surgical applicability of neuroArm.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».