Influence of the Chin-Down and Chin-Tuck Maneuver on the Swallowing Kinematics of Healthy Adults

Abstract The purpose of the study was to investigate the influence of the chin-tuck maneuver on the movements of swallowing-related structures in healthy subjects and formulate standard instructions for the maneuver. A total of 40 healthy volunteers (20 men and 20 women) swallowed 10 mL of diluted barium solution in a ''normal and comfortable'' position (NEUT), a comfortable chin-down position (DOWN), and a strict chin-tuck position (TUCK). Resting state anatomy and kinematic changes were analyzed and compared between postures. Although angles of anterior cervical flexion were comparable between DOWN (46.65 ± 9.69 degrees) and TUCK (43.27 ± 12.20), the chin-to-spine distance was significantly shorter in TUCK than in other positions. Only TUCK showed a significantly shorter anteroposterior diameter of the laryngeal inlet (TUCK vs. NEUT, 14.0 ± 4.3 vs. 16.3 ± 5.0 mm) and the oropharynx (18.8 ± 3.1 vs. 20.5 ± 2.8 mm) at rest. The maximal horizontal displacement of the hyoid bone was significantly less in TUCK (9.6 ± 3.0 mm) than in NEUT (12.6 ± 2.6 mm; p \ 0.01) or DOWN (12.1 ± 3.0 mm; p \ 0.01). TUCK facilitated movement of the epiglottic base upward (TUCK vs. NEUT, 15.8 ± 4.7 vs. 13.3 ± 4.5 mm; p \ 0.01). In contrast, DOWN increased the horizontal excursion of the epiglottic base and reduced movement of the vocal cords. These results quantitatively elucidated the biomechanical influences of the chin-tuck maneuver including reduced horizontal movement of the hyoid bone, facilitation of vertical movement of the epiglottic base, and narrowing of the airway entrance. Comparing DOWN and TUCK, only TUCK induced significant changes in the airway entrance, hyoid movement, and epiglottic base retraction

Diagnosis of osteoporotic vertebral compression fractures and fracture level detection using multitask learning with U-Net in lumbar spine lateral radiographs

Recent studies of automatic diagnosis of vertebral compression fractures (VCFs) using deep learning mainly focus on segmentation and vertebral level detection in lumbar spine lateral radiographs (LSLRs). Herein, we developed a model for simultaneous VCF diagnosis and vertebral level detection without using adjacent vertebral bodies. In total, 1102 patients with VCF, 1171 controls were enrolled. The 1865, 208, and 198 LSLRS were divided into training, validation, and test dataset. A ground truth label with a 4-point trapezoidal shape was made based on radiological reports showing normal or VCF at some vertebral level. We applied a modified U-Net architecture, in which decoders were trained to detect VCF and vertebral levels, sharing the same encoder. The multi-task model was significantly better than the single-task model in sensitivity and area under the receiver operating characteristic curve. In the internal dataset, the accuracy, sensitivity, and specificity of fracture detection per patient or vertebral body were 0.929, 0.944, and 0.917 or 0.947, 0.628, and 0.977, respectively. In external validation, those of fracture detection per patient or vertebral body were 0.713, 0.979, and 0.447 or 0.828, 0.936, and 0.820, respectively. The success rates were 96 % and 94 % for vertebral level detection in internal and external validation, respectively. The multi-task-shared encoder was significantly better than the single-task encoder. Furthermore, both fracture and vertebral level detection was good in internal and external validation. Our deep learning model may help radiologists perform real-life medical examinations