Augmented Reality-Guided Mastoidectomy Simulation: A Randomized Controlled Trial Assessing Surgical Proficiency.

OBJECTIVE: Mastoidectomy surgical training is challenging due to the complex nature of the anatomical structures involved. Traditional training methods based on direct patient care and cadaveric temporal bone training have practical shortcomings. 3D-printed temporal bone models and augmented reality (AR) have emerged as promising solutions, particularly for mastoidectomy surgery, which demands an understanding of intricate anatomical structures. Evidence is needed to explore the potential of AR technology in addressing these training challenges. METHODS: 21 medical students in their clinical clerkship were recruited for this prospective, randomized controlled trial assessing mastoidectomy skills. The participants were randomly assigned to the AR group, which received real-time guidance during drilling on 3D-printed temporal bone models, or to the control group, which received traditional training methods. Skills were assessed on a modified Welling scale and evaluated independently by two senior otologists. RESULTS: The AR group outperformed the control group, with a mean overall drilling score of 19.5 out of 25, compared with the control group's score of 12 (p < 0.01). The AR group was significantly better at defining mastoidectomy margins (p < 0.01), exposing the antrum, preserving the lateral semicircular canal (p < 0.05), sharpening the sinodural angle (p < 0.01), exposing the tegmen and attic, preserving the ossicles (p < 0.01), and thinning and preserving the external auditory canal (p < 0.05). CONCLUSION: AR simulation in mastoidectomy, even in a single session, improved the proficiency of novice surgeons compared with traditional methods. LEVEL OF EVIDENCE: NA Laryngoscope, 2024.

Pre-Operative Planning of a DCR Surgery Using Virtual Reality.

PURPOSE: To assess a novel Virtual Reality (VR) tool designed to enhance understanding of the nasal anatomy in patients eligible for DCR surgery. METHODS: Preoperative Computed Tomography (CT) scans of the orbit were obtained and loaded as DICOM (Digital Imaging and Communications in Medicine) files onto the D2P software (3D Systems Inc. Littleton, CO) for tissue segmentation and 3D model preparation. Segmentation was performed on several anatomical structures, including the skull, lacrimal sac, nasal septum, inferior and middle turbinate. The resulting 3D model was visualized using a VR headset. After completing the segmentation procedure, ten cases were evaluated by a panel of six surgeons, including both senior and resident physicians from ENT and oculoplastic specialties. RESULTS: The dataset under examination comprised images from 10 preoperative CT scans of the orbits of patients eligible for Endo-DCR. When evaluating the CT using the VR tool, in 73.3% of the cases ENT surgeons were right about the side of pathology, while only 43.3% ophthalmologists were right (chi-square, p = .018). In 72.8% of the cases ENT surgeons were evaluated right that there is a septum deviation, while only in 47.2% of the cases the ophthalmologists were right (chi-square, p = .094).When evaluating the CT using the VR tool, in 60% of the cases consultants were right about the pathology, while 57.7% of the residents were right (chi-square, p = .853). In 81.7% of the cases consultants were evaluated right that there is a septum deviation, while only in 58.3% of the cases the ophthalmologists were right (chi-square, p = .198). DISCUSSION: ENT surgeons, as well as consultants, interpreted the CT better than the ophthalmologists and residents. Surprisingly, the VR system did not help them to interpret the CT better. Further, more extensive studies should be done to build a VR system that assists in the correct interpretation of the preoperative CT before DCR surgery as well as during DCR surgery.

Machine learning in cardiac stress test interpretation: a systematic review.

Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted.