Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs.

OBJECTIVES: Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)-based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection. METHODS: We used 1200 weight-bearing lateral foot radiographs from young adult Korean males for the model development. An experienced orthopedic surgeon identified 22 radiographic landmarks and measured three angles for flatfoot diagnosis that served as the ground truth (GT). Another orthopedic surgeon (OS) and a general physician (GP) independently identified the landmarks of the test dataset and measured the angles using the same method. External validation was performed using 100 and 17 radiographs acquired from a tertiary referral center and a public database, respectively. RESULTS: The DLM showed smaller absolute average errors from the GT for the three angle measurements for flatfoot diagnosis compared with both human observers. Under the guidance of the DLM, the average errors of observers OS and GP decreased from 2.35° ± 3.01° to 1.55° ± 2.09° and from 1.99° ± 2.76° to 1.56° ± 2.19°, respectively (both p < 0.001). The total measurement time decreased from 195 to 135 min in observer OS and from 205 to 155 min in observer GP. The absolute average errors of the DLM in the external validation sets were similar or superior to those of human observers in the original test dataset. CONCLUSIONS: Our CNN model had significantly better accuracy and reliability than human observers in diagnosing flatfoot, and notably improved the accuracy and reliability of human observers. KEY POINTS: • Development of deep learning model (DLM) that allows automated angle measurements for landmark detection based on 1200 weight-bearing lateral radiographs for diagnosing flatfoot. • Our DLM showed smaller absolute average errors for flatfoot diagnosis compared with two human observers. • Under the guidance of the model, the average errors of two human observers decreased and total measurement time also decreased from 195 to 135 min and from 205 to 155 min.

Tin Oxide/Vertically Aligned Graphene Hybrid Electrodes Prepared by Sonication-Assisted Sequential Chemical Bath Deposition for High-Performance Supercapacitors.

Hybrid electrodes comprising metal oxides and vertically aligned graphene (VAG) are promising for high-performance supercapacitor applications because they enhance the synergistic effect owing to the large contact area between the two constituent materials. However, it is difficult to form metal oxides (MOs) up to the inner surface of a VAG electrode with a narrow inlet using conventional synthesis methods. Herein, we report a facile approach to fabricate SnO2 nanoparticle-decorated VAG electrodes (SnO2@VAG) with excellent areal capacitance and cyclic stability using sonication-assisted sequential chemical bath deposition (S-SCBD). The sonication treatment during the MO decoration process induced a cavitation effect at the narrow inlet of the VAG electrode, allowing the precursor solution to reach the inside of the VAG surface. Furthermore, the sonication treatment promoted MO nucleation on the entire VAG surface. Thus, the SnO2 nanoparticles uniformly covered the entire electrode surface after the S-SCBD process. SnO2@VAG exhibited an outstanding areal capacitance (4.40 F cm-2) up to 58% higher than that of VAG electrodes. The symmetric supercapacitor with SnO2@VAG electrodes showed an excellent areal capacitance (2.13 F cm-2) and a cyclic stability of 90% after 2000 cycles. These results suggest a new avenue for sonication-assisted fabrication of hybrid electrodes in the field of energy storage.

Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot.

Landmark detection in flatfoot radiographs is crucial in analyzing foot deformity. Here, we evaluated the accuracy and efficiency of the automated identification of flatfoot landmarks using a newly developed cascade convolutional neural network (CNN) algorithm, Flatfoot Landmarks AnnoTating Network (FlatNet). A total of 1200 consecutive weight-bearing lateral radiographs of the foot were acquired. The first 1050 radiographs were used as the training and tuning, and the following 150 radiographs were used as the test sets, respectively. An expert orthopedic surgeon (A) manually labeled ground truths for twenty-five anatomical landmarks. Two orthopedic surgeons (A and B, each with eight years of clinical experience) and a general physician (GP) independently identified the landmarks of the test sets using the same method. After two weeks, observers B and GP independently identified the landmarks once again using the developed deep learning CNN model (DLm). The X- and Y-coordinates and the mean absolute distance were evaluated. The average differences (mm) from the ground truth were 0.60 ± 0.57, 1.37 ± 1.28, and 1.05 ± 1.23 for the X-coordinate, and 0.46 ± 0.59, 0.97 ± 0.98, and 0.73 ± 0.90 for the Y-coordinate in DLm, B, and GP, respectively. The average differences (mm) from the ground truth were 0.84 ± 0.73, 1.90 ± 1.34, and 1.42 ± 1.40 for the absolute distance in DLm, B, and GP, respectively. Under the guidance of the DLm, the overall differences (mm) from the ground truth were enhanced to 0.87 ± 1.21, 0.69 ± 0.74, and 1.24 ± 1.31 for the X-coordinate, Y-coordinate, and absolute distance, respectively, for observer B. The differences were also enhanced to 0.74 ± 0.73, 0.57 ± 0.63, and 1.04 ± 0.85 for observer GP. The newly developed FlatNet exhibited better accuracy and reliability than the observers. Furthermore, under the FlatNet guidance, the accuracy and reliability of the human observers generally improved.

The effects of backward, upward, rightward pressure maneuver for intubation using the OptiscopeTM: a retrospective study.

BACKGROUND: The OptiscopeTM and the backward, upward, rightward pressure (BURP) maneuver are widely used in clinical practice because the BURP maneuver facilitates intubation by improving visualization of the larynx. However, the effect of the BURP maneuver is unclear when using the OptiscopeTM. Therefore, we retrospectively investigated the effect of the BURP maneuver on intubation using the OptiscopeTM. METHODS: Sixty-eight patients intubated with the OptiscopeTM were enrolled. We used the BURP maneuver in Group A (n = 33) and the conventional maneuver (which does not use the BURP maneuver) in Group B (n = 35). BURP application status was a binary variable representing whether the BURP maneuver was used during the intubation. A multiple linear regression analysis was performed to assess the effects of the BURP application status on intubation time controlling for body mass index, preoperative dental injury status, obstructive sleep apnea history, thyromental distance, sternomental distance, interincisor distance, history of neck rotation restriction, and Mallampati classification. RESULTS: There was no difference in the intubation time between the two groups. According to the regression model (R2 = 0.308, P = 0.007), the BURP maneuver (Group A) decreased the intubation time by 6.089 seconds (95% confidence interval 1.303-10.875, P = 0.014) compared to Group B.