Automating medical simulations.

OBJECTIVE: This study aims to explore speech as an alternative modality for human activity recognition (HAR) in medical settings. While current HAR technologies rely on video and sensory modalities, they are often unsuitable for the medical environment due to interference from medical personnel, privacy concerns, and environmental limitations. Therefore, we propose an end-to-end, fully automatic objective checklist validation framework that utilizes medical personnel's uttered speech to recognize and document the executed actions in a checklist format. METHODS: Our framework records, processes, and analyzes medical personnel's speech to extract valuable information about performed actions. This information is then used to fill the corresponding rubrics in the checklist automatically. RESULTS: Our approach to activity recognition outperformed the online expert examiner, achieving an F1 score of 0.869 on verbal tasks and an ICC score of 0.822 with an offline examiner. Furthermore, the framework successfully identified communication failures and medical errors made by physicians and nurses. CONCLUSION: Implementing a speech-based framework in medical settings, such as the emergency room and operation room, holds promise for improving care delivery and enabling the development of automated assistive technologies in various medical domains. By leveraging speech as a modality for HAR, we can overcome the limitations of existing technologies and enhance workflow efficiency and patient safety.

Modelling the resonant frequency associated with the spatio-temporal evolution of the bone-dental implant interface.

Dental implant stability is greatly affected by the mechanical properties of the bone-implant interface (BII), and it is key to long-term successful osseointegration. Implant stability is often evaluated using the Resonant Frequency Analysis (RFA) method, and also by the quality of this interface, namely the bone-implant contact (BIC). True to this day, there is a scarcity of models tying BIC, RFA and a spatially and mechanically evolving BII. In this paper, based on the contact/distance osteogenesis concept, a novel numerical spatio-temporal model of the implant, surrounding bone and evolving interface, was developed to assess the evolution of the interfacial stresses on the one hand and the corresponding resonant frequencies on the other. We postulate that, since the BIC percentage reaches saturation over a very short time, long before densification of the interface, it becomes irrelevant as to load transmission between the implant and the bone due to the existence of an open gap. Gap closure is the factor that provides continuity between the implant and the surrounding bone. The results of the calculated RFA evolution match and provide an explanation for the multiple clinical observations of a sharp initial decline in RFA, followed by a gradual increase and plateau formation. STATEMENT OF SIGNIFICANCE: A novel three-dimensional numerical model of an evolving bone-dental implant interface (BII) is presented. The spatio-temporal evolution of the bone-implant contact (BIC) and the BII, based on contact/distance (CO/DO) osteogenesis, is modeled. A central outcome is that, until BII maturation into a solid continuous bone (no open gap between CO-DO fronts), the bone-implant load transfer is hampered, irrespective of the BIC. The resonant frequencies' evolution of the jawbone-BII-implant is calculated to reproduce the well-established implant stability analysis based on the Resonant Frequency Analysis. The results resemble those reported clinically, and here too, the determinant transition occurs only after interfacial gap closure. Those results should motivate clinicians to re-consider structural continuity of the BII rather than the BIC only.

Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

PURPOSE: Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI. METHODS: A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A fivefold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel-based classification of the tumor into enhanced, non-enhanced, and cystic components was performed using fuzzy c-means clustering. RESULTS: The results of the automatic tumor segmentation were: mean dice score = 0.736 ± 0.025, precision = 0.918 ± 0.014, and recall = 0.635 ± 0.039 for the validation data, and dice score = 0.761 ± 0.011, precision = 0.794 ± 0.028, and recall = 0.742 ± 0.012 for the test data. The accuracy of the voxel-based classification of tumor components was 0.94, with precision = 0.89, 0.97, and 0.85, and recall = 1.00, 0.79, and 0.94 for the non-enhanced, enhanced, and cystic components, respectively. CONCLUSION: This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.