Yuka Miura

Aims: To visualize the process of esophageal speech in patients who underwent laryngectomy using diagnostic ultrasound.Material and methods: Three patients with different surgical reconstructions (total laryngectomy, pharyngo-laryngo-esophagectomy with gastric tube reconstruction, and pharyngo-laryngo-cervical-esophagectomy with free jejunal reconstruction) were studied. Ultrasound with a linear probe observed organ vibration and dimensions at the 5th-7th cervical vertebrae during rest, air swallowing, and phonation. Cross-sectional lateral and anteroposterior diameters were measured.Results: Lateral diameters (mm) during rest, air swallowing, and sound production were 15, 18, and 15 for the esophagus; 24, 27, and 23 for the gastric tube; and 19, 32, and 20 for the jejunal graft, respectively. Longitudinal motion of the esophagus and gastric tube lumen coincided with artifact, while jejunal villi movements were noted. Air swallowing induced an oval organ shape, and lateral diameters shortened during sound production.Conclusions: Vibratory activity spanning over 4 cm was observed at the 5th-7th cervical vertebrae. Each organ displayed distinct vibration patterns, with luminal shape changes during sound production. These findings offer new insights into the biomechanics of esophageal speech and understanding of postoperative rehabilitation.

This study aimed to develop an artificial intelligence-based classification system using ultrasound images obtained via a transgluteal cleft scanning approach for detecting fecal retention in the lower rectum. The goal was to support accurate, objective constipation assessment by nurses in home care settings, where traditional diagnostic tools are often unavailable. Ultrasound videos of the lower rectum were collected from 24 patients undergoing dialysis at a mixed-care hospital. From 90 videos, 2,855 still images were extracted and labeled by expert sonographers based on the presence or absence of hyperechoic areas indicating fecal retention. A deep learning segmentation model using U-Net with a ResNeXt-50 encoder was trained and evaluated. Performance was measured using the intersection over union threshold of 0.5 to define true positives. Accuracy, sensitivity, and specificity were calculated on a test dataset of 758 images. Among the test images, 376 (49.6%) showed fecal retention. The AI system achieved a sensitivity of 81.6%, specificity of 84.0%, and overall accuracy of 82.8%. The mean IoU was 0.601 ± 0.185, indicating a high level of agreement between expert annotations and AI-generated predictions. The tool reliably detected fecal retention in ultrasound images obtained using the transgluteal cleft approach, which overcomes limitations of traditional transabdominal scanning caused by obesity, bladder emptying, or bowel gas. The proposed AI-assisted ultrasound system showed high diagnostic performance in identifying fecal retention in the lower rectum. It may enable non-specialist nurses to assess constipation more safely and accurately, particularly in home-care environments. This technology has the potential to reduce unnecessary laxative use and invasive interventions, ultimately improving the quality of bowel care for older adults with impaired communication or mobility.

This study aimed to examine the constipation detection accuracy of ultrasonography for elderly people who cannot clearly communicate their symptoms. We searched for studies, including longitudinal observational studies, cross-sectional studies, and case-control studies, on MEDLINE（via PubMed）, The Cochrane Library/ CENTRAL, Ichushi-Web, Embase, CINAHL, and the Cochrane Database of Systematic Reviews. We included studies that investigated the detection accuracy of ultrasonography for fecal retention in English and Japanese. The sensitivity and specificity of ultrasonography as a constipation assessment tool were calculated in each included study, and their pooled estimated values were determined. Six studies were included in the qualitative analysis. Ultrasonography was conducted once a day or before defecation. The pooled sensitivity and specificity of detecting constipation by ultrasonography were 0.93（95% CI: 0.63-0.99）and 0.81（95% CI: 0.60-0.92）, respectively. However, the overall certainty of evidence in the included studies was very low. Ultrasonography showed a high constipation detection accuracy, which may be a useful assessment tool in clinical settings because of its portability and noninvasiveness. Future studies are needed to improve the evidence level.

BACKGROUND: Transabdominal ultrasound is used to detect fecal impaction, but the rectum is difficult to visualize without bladder urine or with gastrointestinal gas. OBJECTIVE: We developed a transgluteal cleft approach that is unaffected by these factors and sought to determine if our ultrasound method could detect and classify fecal matter in the lower rectum using this approach. METHODS: We classified ultrasound images from hospitalized patients into four groups: Group 1 (bowed and rock-like echogenic areas), Group 2 (irregular and cotton candy-like hyperechoic areas), Group 3 (flat and mousse-like hyperechoic areas), and Group 4 (linear echogenic areas in the lumen). Stool characteristics were classified as hard, normal, and muddy/watery. Sensitivity and specificity were determined based on fecal impaction and stool classification accuracy. RESULTS: We obtained 129 ultrasound images of 23 patients. The sensitivity and specificity for fecal retention in the rectum were both 100.0%. The recall rates were 71.8% for Group 1, 93.1% for Group 2, 100.0% for Group 3, and 100.0% for Group 4. The precision rates were 96.6% for Group 1, 71.1% for Group 2, 88.9% for Group 3, and 100.0% for Group 4. Our method was 89.9% accurate overall. CONCLUSION: Transgluteal cleft approach ultrasound scanning can detect and classify fecal properties with high accuracy.