Naomi Yagi

Vacuum swallowing is a unique method for improving the pharyngeal passage of a bolus by creating subatmospheric negative pressure in the esophagus. However, whether healthy individuals and other patients with dysphagia can reproduce vacuum swallowing remains unclear. Therefore, this study aimed to assess whether healthy individuals verified using high-resolution manometry (HRM) could reproduce vacuum swallowing and evaluate its safety using a swallowing and breathing monitoring system (SBMS). Two healthy individuals who mastered vacuum swallowing taught this method to 12 healthy individuals, who performed normal and vacuum swallowing with 5 mL of water five times each. The minimum esophageal pressure and the maximum pressure of the lower esophageal sphincter (LES) were evaluated during each swallow using the HRM. Additionally, respiratory-swallowing coordination was evaluated using the SBMS. Ten individuals reproduced vacuum swallowing, and a total of 50 vacuum swallows were analyzed. The minimum esophageal pressure (-15.0 ± 4.9 vs. -46.6 ± 16.7 mmHg; P < 0.001) was significantly lower, and the maximum pressure of the LES (25.4 ± 37.7 vs. 159.5 ± 83.6 mmHg; P < 0.001) was significantly higher during vacuum swallowing. The frequencies of the I-SW and SW-I patterns in vacuum swallowing were 38.9% and 0%, respectively, using the SBMS. Vacuum swallowing could be reproduced safely in healthy participants with instruction. Therefore, instructing exhalation before and after vacuum swallowing is recommended to prevent aspiration.

PURPOSE: Swallowing dysfunction and the risk of aspiration pneumonia are frequent clinical problems in the treatment of head and neck squamous cell carcinomas (HNSCCs). Breathing-swallowing coordination is an important factor in evaluating the risk of aspiration pneumonia. To investigate breathing-swallowing discoordination after chemoradiotherapy (CRT), we monitored respiration and swallowing activity before and after CRT in patients with HNSCCs. METHODS: Non-invasive swallowing monitoring was prospectively performed in 25 patients with HNSCCs treated with CRT and grade 1 or lower radiation-induced dermatitis. Videoendoscopy, videofluoroscopy, Food Intake LEVEL Scale, and patient-reported swallowing difficulties were assessed. RESULTS: Of the 25 patients selected for this study, four dropped out due to radiation-induced dermatitis. The remaining 21 patients were analyzed using a monitoring system before and after CRT. For each of the 21 patients, 405 swallows were analyzed. Swallowing latency and pause duration after the CRT were significantly extended compared to those before the CRT. In the analysis of each swallowing pattern, swallowing immediately followed by inspiration (SW-I pattern), reflecting breathing-swallowing discoordination, was observed more frequently after CRT (p = 0.0001). In 11 patients, the SW-I pattern was observed more frequently compared to that before the CRT (p = 0.00139). One patient developed aspiration pneumonia at 12 and 23 months after the CRT. CONCLUSION: The results of this preliminary study indicate that breathing-swallowing discoordination tends to increase after CRT and could be involved in aspiration pneumonia. This non-invasive method may be useful for screening swallowing dysfunction and its potential risks.

Pelvic fractures pose significant challenges in medical diagnosis due to the complex structure of the pelvic bones. Timely diagnosis of pelvic fractures is critical to reduce complications and mortality rates. While computed tomography (CT) is highly accurate in detecting pelvic fractures, the initial diagnostic procedure usually involves pelvic X-rays (PXR). In recent years, many deep learning-based methods have been developed utilizing ImageNet-based transfer learning for diagnosing hip and pelvic fractures. However, the ImageNet dataset contains natural RGB images which are different than PXR. In this study, we proposed a two-step transfer learning approach that improved the diagnosis of pelvic fractures in PXR images. The first step involved training a deep convolutional neural network (DCNN) using synthesized PXR images derived from 3D-CT by digitally reconstructed radiographs (DRR). In the second step, the classification layers of the DCNN were fine-tuned using acquired PXR images. The performance of the proposed method was compared with the conventional ImageNet-based transfer learning method. Experimental results demonstrated that the proposed DRR-based method, using 20 synthesized PXR images for each CT, achieved superior performance with the area under the receiver operating characteristic curves (AUROCs) of 0.9327 and 0.8014 for visible and invisible fractures, respectively. The ImageNet-based method yields AUROCs of 0.8908 and 0.7308 for visible and invisible fractures, respectively.