The purpose of this study was to evaluate the typical ultrasonographic findings of transient small bowel intussusception (SBI) and to differentiate it from ileocolic intussusception (ICI) in paediatrics. 22 transient SBI (male:female = 13:9, age: 7-132 months (mean 38 months)) and 27 ICI (male:female = 19:8, age: 1-60 months (mean 13 months)) patients diagnosed on ultrasonography were retrospectively evaluated. The findings of location, diameter, thickness of outer rim, and inclusion of mesenteric lymph nodes within intussuscipiens were compared. In the transient SBI, the head of intussusception was located in the right lower quadrant (RLQ) in 11 (50%), the right upper quadrant (RUQ) in 2 (9.1%) and the periumbilical area in 9 (40.9%) cases. The anteroposterior (AP) diameter ranged from 0.84-2.4 cm (mean 1.38 cm), and thickness of outer rim ranged from 0.10-0.34 cm (mean 0.26 cm). No mesenteric lymph nodes were contained within the intussuscipiens. In the ICI, the head was located in the RUQ in 17 (63%), the epigastrium in 7 (25.9%) and the left upper quadrant in 3 (11.1%) cases. The AP diameter ranged from 1.89-3.32 cm (mean 2.53 cm), and the thickness of the outer rim ranged from 0.30-0.86 cm (mean 0.53 cm). Mesenteric lymph nodes were contained within the intussuscipiens in 26 (96.3%) cases. In conclusion, when compared with ICI, the transient SBI occurs predominantly in the RLQ or periumbilical region, has a smaller AP diameter, a thinner outer rim, and dose not contain mesenteric lymph nodes.

The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.

This study aimed to develop a method for detection of femoral neck fracture (FNF) including displaced and non-displaced fractures using convolutional neural network (CNN) with plain X-ray and to validate its use across hospitals through internal and external validation sets. This is a retrospective study using hip and pelvic anteroposterior films for training and detecting femoral neck fracture through residual neural network (ResNet) 18 with convolutional block attention module (CBAM) + + . The study was performed at two tertiary hospitals between February and May 2020 and used data from January 2005 to December 2018. Our primary outcome was favorable performance for diagnosis of femoral neck fracture from negative studies in our dataset. We described the outcomes as area under the receiver operating characteristic curve (AUC), accuracy, Youden index, sensitivity, and specificity. A total of 4,189 images that contained 1,109 positive images (332 non-displaced and 777 displaced) and 3,080 negative images were collected from two hospitals. The test values after training with one hospital dataset were 0.999 AUC, 0.986 accuracy, 0.960 Youden index, and 0.966 sensitivity, and 0.993 specificity. Values of external validation with the other hospital dataset were 0.977, 0.971, 0.920, 0.939, and 0.982, respectively. Values of merged hospital datasets were 0.987, 0.983, 0.960, 0.973, and 0.987, respectively. A CNN algorithm for FNF detection in both displaced and non-displaced fractures using plain X-rays could be used in other hospitals to screen for FNF after training with images from the hospital of interest. KeywordsFemur • Fracture • Deep learning • Machine learning • Artificial intelligence • AI Abbreviations FNF Femoral neck fracture CNN Convolution neural network ResNet Residual neural network CBAM Convolutional block attention module AUC Area under the receiver operating characteristic curve CT Computed tomography MRI Magnetic resonance imaging Junwon Bae and Sangjoon Yu contributed this work equally as first author.

This study aimed to verify a deep convolutional neural network (CNN) algorithm to detect intussusception in children using a human-annotated data set of plain abdominal X-rays from affected children. From January 2005 to August 2019, 1449 images were collected from plain abdominal X-rays of patients ≤ 6 years old who were diagnosed with intussusception while 9935 images were collected from patients without intussusception from three tertiary academic hospitals (A, B, and C data sets). Single Shot MultiBox Detector and ResNet were used for abdominal detection and intussusception classification, respectively. The diagnostic performance of the algorithm was analysed using internal and external validation tests. The internal test values after training with two hospital data sets were 0.946 to 0.971 for the area under the receiver operating characteristic curve (AUC), 0.927 to 0.952 for the highest accuracy, and 0.764 to 0.848 for the highest Youden index. The values from external test using the remaining data set were all lower (P-value < 0.001). The mean values of the internal test with all data sets were 0.935 and 0.743 for the AUC and Youden Index, respectively. Detection of intussusception by deep CNN and plain abdominal X-rays could aid in screening for intussusception in children.

Background: The purpose of this study was to determine if there were changes in bystanders' chest compression performance and activation of emergency medical services in geriatric and out-of-hospital cardiac patients following the institution of the 2010 International Resuscitation Guidelines and 2008 Good Samaritan Law in South Korea. Methods: This is a retrospective observational study using medical records, and including patient charts and an Utstein Style database in a tertiary hospital. We analyzed the existence of chest compression performance by bystanders, the required time from recognition of cardiac arrest to activation of 119 for emergency medicine services, and the required time from activation of 119 to arrival on the scene from 2005-2014. The data were compared after dividing the years into 2 groups: 2005-2009 and 2010-2014. Results: Of 317 geriatric and out-of-hospital cardiac arrest patients, 261 were eligible for this study. Twelve cases were excluded, and a total of 249 were analyzed. Bystander-initiated chest compression was higher from