三浦 由佳

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.

本研究では症状を訴えられない高齢者のための超音波検査を用いた便秘の検出精度に関して調べることを目的とした。英語および日本語の文献で便秘の検出における超音波検査の診断精度を示す研究（縦断的観察研究、横断的研究、症例対照研究）を、MEDLINE、The Cochrane Library/CENTRAL、医中誌Web、Embase、CINAHL、およびCochrane Database of Systematic Reviewで検索した。それぞれの研究において、便秘評価ツールとしての超音波検査の感度と特異度を計算し、統合した推定の感度特異度を算出した。その結果、6 つの研究が対象として含まれ、超音波検査は1 日1 回、または排便前に実施されていた。超音波検査による便秘検出の統合した感度と特異度は、それぞれ0.93（95% CI: 0.63-0.99）および0.81（95% CI: 0.60-0.92）であった。対象となった研究の全体的なエビデンスの確実性は、「不明」なバイアスリスクが50％以上評価領域に存在し、また研究間で結果が異なっており、研究数が10件未満であったため、非常に低いと評価された。したがって、超音波検査は便秘検出において高い診断精度を示しており、その携帯性および非侵襲性から臨床現場で有用な評価ツールとなる可能性があると考えられる。しかし、エビデンスレベルが低いため、今後さらなる研究が必要である。

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.