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Objectives: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movements of the tympanic membrane and part of the ossicles, analyzing VPO images was expected to be useful in predicting the presence of middle ear transmission problems. Using a convolutional neural network (CNN), a deep neural network implementing computer vision, this preliminary study aimed to create a deep learning model that detects the presence of an air-bone gap, conductive component of hearing loss, by analyzing VPO findings., Design: The medical records of adult patients who underwent VPO tests and pure-tone audiometry (PTA) on the same day were reviewed for enrollment. Conductive hearing loss was defined as an average air-bone gap of more than 10 dB at 0.5, 1, 2, and 4 kHz on PTA. Two significant images from the original VPO videos, at the most medial position on positive pressure and the most laterally displaced position on negative pressure, were used for the analysis. Applying multi-column CNN architectures with individual backbones of pretrained CNN versions, the performance of each model was evaluated and compared for Inception-v3, VGG-16 or ResNet-50. The diagnostic accuracy predicting the presence of conductive component of hearing loss of the selected deep learning algorithm used was compared with experienced otologists., Results: The conductive hearing loss group consisted of 57 cases (mean air-bone gap = 25 ± 8 dB): 21 ears with effusion, 14 ears with malleus-incus fixation, 15 ears with stapes fixation including otosclerosis, one ear with a loose incus-stapes joint, 3 cases with adhesive otitis media, and 3 ears with middle ear masses including congenital cholesteatoma. The control group consisted of 76 cases with normal hearing thresholds without air-bone gaps. A total of 1130 original images including repeated measurements were obtained for the analysis. Of the various network architectures designed, the best was to feed each of the images into the individual backbones of Inception-v3 (three-column architecture) and concatenate the feature maps after the last convolutional layer from each column. In the selected model, the average performance of 10-fold cross-validation in predicting conductive hearing loss was 0.972 mean areas under the curve (mAUC), 91.6% sensitivity, 96.0% specificity, 94.4% positive predictive value, 93.9% negative predictive value, and 94.1% accuracy, which was superior to that of experienced otologists, whose performance had 0.773 mAUC and 79.0% accuracy on average. The algorithm detected over 85% of cases with stapes fixations or ossicular chain problems other than malleus-incus fixations. Visualization of the region of interest in the deep learning model revealed that the algorithm made decisions generally based on findings in the malleus and nearby tympanic membrane., Conclusions: In this preliminary study, the deep learning algorithm created to analyze VPO images successfully detected the presence of conductive hearing losses caused by middle ear effusion, ossicular fixation, otosclerosis, and adhesive otitis media. Interpretation of VPO using the deep learning algorithm showed promise as a diagnostic tool to differentiate conductive hearing loss from sensorineural hearing loss, which would be especially useful for patients with poor cooperation., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2022 The Authors. Ear & Hearing is published on behalf of the American Auditory Society, by Wolters Kluwer Health, Inc.)

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., (© 2021. Society for Imaging Informatics in Medicine.)

Learning-based data structures, such as a learned Bloom filter and a learned functional Bloom filter (L-FBF), have recently been proposed to replace traditional structures. However, using these structures for dynamic data processing is difficult because a specific element cannot be deleted from a trained model. A counting Bloom filter with return values (rCBF) is a more efficient key–value structure than a functional Bloom filter (FBF) for repetitive insertions and deletions. In this study, we propose a learned rCBF (L-rCBF) comprising a model, a Bloom filter, and an rCBF and the deletion algorithm for the L-rCBF. To delete a specific element from the L-rCBF, two different operations are performed according to four different cases. In the experiments, the proposed L-rCBF is compared with a single rCBF and an L-FBF in terms of undeletables and search failures, and this comparison is conducted using two different models. In addition, we present a theoretical analysis of the rCBF with experimental results to demonstrate that a structure with an rCBF is more suitable for dynamic data than a structure with an FBF. [ABSTRACT FROM AUTHOR]

Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop an algorithm for multilabel classification according to the distance from carina to endotracheal tube (ETT) tip (absence, shallow > 70 mm, 30 mm ≤ proper ≤ 70 mm, and deep position < 30 mm) with the application of automatic segmentation of the trachea and the ETT on chest radiographs using deep convolutional neural network (CNN). Methods: This study was a retrospective study using plain chest radiographs. We segmented the trachea and the ETT on images and labeled the classification of the ETT position. We proposed models for the classification of the ETT position using EfficientNet B0 with the application of automatic segmentation using Mask R-CNN and ResNet50. Primary outcomes were favorable performance for automatic segmentation and four-label classification through five-fold validation with segmented images and a test with non-segmented images. Results: Of 1985 images, 596 images were manually segmented and consisted of 298 absence, 97 shallow, 100 proper, and 101 deep images according to the ETT position. In five-fold validations with segmented images, Dice coefficients [mean (SD)] between segmented and predicted masks were 0.841 (0.063) for the trachea and 0.893 (0.078) for the ETT, and the accuracy for four-label classification was 0.945 (0.017). In the test for classification with 1389 non-segmented images, overall values were 0.922 for accuracy, 0.843 for precision, 0.843 for sensitivity, 0.922 for specificity, and 0.843 for F1-score. Conclusions: Automatic segmentation of the ETT and trachea images and classification of the ETT position using deep CNN with plain chest radiographs could achieve good performance and improve the physician's performance in deciding the appropriateness of ETT depth. [ABSTRACT FROM AUTHOR]

As a challenging attempt to replace a traditional data structure with a learned model, this paper proposes a learned functional Bloom filter (L-FBF) for a key–value storage. The learned model in the proposed L-FBF learns the characteristics and the distribution of given data and classifies each input. It is shown through theoretical analysis that the L-FBF provides a lower search failure rate than a single FBF in the same memory size, while providing the same semantic guarantees. For model training, character-level neural networks are used with pretrained embeddings. In experiments, four types of different character-level neural networks are trained: a single gated recurrent unit (GRU), two GRUs, a single long short-term memory (LSTM), and a single one-dimensional convolutional neural network (1D-CNN). Experimental results prove the validity of theoretical results, and show that the L-FBF reduces the search failures by 82.8% to 83.9% when compared with a single FBF under the same amount of memory used. [ABSTRACT FROM AUTHOR]