Background: Although total intravenous anesthesia (TIVA) with propofol and remifentanil is frequently used to optimize intraoperative neurophysiological monitoring (IONM), the exact effect of remimazolam on IONM remains unknown. Here, we compared the effects of propofol and remimazolam along with remifentanil on IONM during TIVA. Methods: In this prospective, double-blind, randomized controlled trial, 64 patients requiring IONM during cervical spine surgery were administered either propofol (Group P) or remimazolam (Group R). The preoperative latencies of the somatosensory-evoked potentials (SEP; N20 for the median nerve and P37 for the tibial nerve) were measured. SEP latencies and amplitudes and motor-evoked potential (MEP) amplitudes were measured 30 min after anesthetic induction (T1), 30 min after surgical incision (T2), after laminectomy or discectomy (T3), immediately after plate insertion or pedicle screw fixation (T4), and before surgical wound closure (T5). The primary outcome was the between-group difference in the N20 latency changes measured at T1 and preoperatively. Results: The change in SEP latencies including N20 and P37 at T1 compared with preoperative time was not significantly different between Groups P and R. Except for the amplitude of the right abductor brevis, there was no significant group-by-time interaction effect for intraoperative MEP amplitudes or SEP latencies and amplitudes. Conclusions TIVA with remimazolam and remifentanil for cervical spine surgery yielded stable IONM, comparable to those observed with conventional TIVA with propofol and remifentanil. Further clinical trials are needed in other surgical contexts and with more diverse patient populations to determine the effects of remimazolam on IONM.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Osteoporosis management in post-menopausal women focuses on fracture prevention, with denosumab as a key therapeutic option. Despite its proven efficacy in reducing fracture risk and increasing bone mineral density (BMD) over 10 years, its long-term impact remains uncertain. We evaluated the literature on its efficacy and safety beyond the initial decade. Clinical trials and real-world studies confirm denosumab’s sustained efficacy, especially in lumbar spine BMD, with hip BMD stabilizing. Concerns about adverse events (AEs) like hypocalcemia and osteonecrosis of the jaw necessitate vigilant monitoring. Risks of atypical femoral fractures and malignancies also require attention, despite unclear links to treatment duration. Clinical guidelines for denosumab beyond 10 years are limited, emphasizing the need for careful monitoring. In certain scenarios, such as advanced chronic kidney disease, prolonged denosumab may be required to balance AE risks with fracture prevention benefits. Denosumab shows potential for long-term efficacy in augmenting BMD; however, monitoring for AEs is crucial to guide clinical decision-making effectively.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Osteoporosis management in post-menopausal women focuses on fracture prevention, with denosumab as a key therapeutic option. Despite its proven efficacy in reducing fracture risk and increasing bone mineral density (BMD) over 10 years, its long-term impact remains uncertain. We evaluated the literature on its efficacy and safety beyond the initial decade. Clinical trials and real-world studies confirm denosumab’s sustained efficacy, especially in lumbar spine BMD, with hip BMD stabilizing. Concerns about adverse events (AEs) like hypocalcemia and osteonecrosis of the jaw necessitate vigilant monitoring. Risks of atypical femoral fractures and malignancies also require attention, despite unclear links to treatment duration. Clinical guidelines for denosumab beyond 10 years are limited, emphasizing the need for careful monitoring. In certain scenarios, such as advanced chronic kidney disease, prolonged denosumab may be required to balance AE risks with fracture prevention benefits. Denosumab shows potential for long-term efficacy in augmenting BMD; however, monitoring for AEs is crucial to guide clinical decision-making effectively.

Background: Although total intravenous anesthesia (TIVA) with propofol and remifentanil is frequently used to optimize intraoperative neurophysiological monitoring (IONM), the exact effect of remimazolam on IONM remains unknown. Here, we compared the effects of propofol and remimazolam along with remifentanil on IONM during TIVA. Methods: In this prospective, double-blind, randomized controlled trial, 64 patients requiring IONM during cervical spine surgery were administered either propofol (Group P) or remimazolam (Group R). The preoperative latencies of the somatosensory-evoked potentials (SEP; N20 for the median nerve and P37 for the tibial nerve) were measured. SEP latencies and amplitudes and motor-evoked potential (MEP) amplitudes were measured 30 min after anesthetic induction (T1), 30 min after surgical incision (T2), after laminectomy or discectomy (T3), immediately after plate insertion or pedicle screw fixation (T4), and before surgical wound closure (T5). The primary outcome was the between-group difference in the N20 latency changes measured at T1 and preoperatively. Results: The change in SEP latencies including N20 and P37 at T1 compared with preoperative time was not significantly different between Groups P and R. Except for the amplitude of the right abductor brevis, there was no significant group-by-time interaction effect for intraoperative MEP amplitudes or SEP latencies and amplitudes. Conclusions TIVA with remimazolam and remifentanil for cervical spine surgery yielded stable IONM, comparable to those observed with conventional TIVA with propofol and remifentanil. Further clinical trials are needed in other surgical contexts and with more diverse patient populations to determine the effects of remimazolam on IONM.