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BACKGROUND CONTEXT Computer-aided diagnosis with artificial intelligence (AI) has been used clinically, and ground truth generalizability is important for AI performance in medical image analyses. The AI model was trained on one specific group of older adults (aged≧60) has not yet been shown to work equally well in a younger adult group (aged 18-59). PURPOSE To compare the performance of the developed AI model with ensemble method trained with the ground truth for those aged 60 years or older in identifying vertebral fractures (VFs) on plain lateral radiographs of spine (PLRS) between younger and older adult populations. STUDY DESIGN/SETTING Retrospective analysis of PLRS in a single medical institution PATIENT SAMPLE OUTCOME MEASURES Accuracy, sensitivity, specificity, and interobserver reliability (kappa value) were used to compare diagnostic performance of the AI model and subspecialists’ consensus between the two groups. METHODS Between January 2016 and December 2018, the ground truth of 941 patients (one PLRS per person) aged 60 years and older with 1101 VFs and 6358 normal vertebrae was used to set up the AI model. The framework of the developed AI model includes: object detection with You Only Look Once Version 3 (YOLOv3) at T0-L5 levels in the PLRS, data pre-preprocessing with image-size and quality processing, and AI ensemble model (ResNet34, DenseNet121, and DenseNet201) for identifying or grading VFs. The reported overall accuracy, sensitivity and specificity were 92%, 91% and 93%, respectively, and external validation was also performed. Thereafter, patients diagnosed as VFs and treated in our institution during October 2019 to August 2020 were the study group regardless of age. In total, 258 patients (339 VFs and 1725 normal vertebrae) in the older adult population (mean age 78 ±10.4; range, 60-106) were enrolled. In the younger adult population (mean age 36 ± 9.43; range, 20-49), 106 patients (120 VFs and 728 normal vertebrae) were enrolled. After identification and grading of VFs based on the Genant method with consensus between two subspecialists’, VFs in each PLRS with human labels were defined as the testing dataset. The corresponding CT or MRI scan was used for labeling in the PLRS. The bootstrap method was applied to the testing dataset. RESULTS The model for clinical application, Digital Imaging and Communications in Medicine (DICOM) format, is uploaded directly (available at: http://140.113.114.104/vght_demo/svf-model (grading) and http://14 0.113.114.104/vght demo/svf-model2 (labeling). Overall accuracy, sensitivity and specificity in the older adult population were 93.36% (95% CI 93.34%-93.38%), 88.97% (95% CI 88.59%-88.99%) and 94.26% (95% CI 94.23%-94.29%), respectively. Overall accuracy, sensitivity and specificity in the younger adult population were 93.75% (95% CI 93.7%-93.8%), 65.00% (95% CI 64.33%-65.67%) and 98.49% (95% CI 98.45%-98.52%), respectively. Accuracy reached 100% in VFs grading once the VFs were labeled accurately. The unique pattern of limbus-like VFs, 43 (35.8%) were investigated only in the younger adult population. If limbus-like VFs from the dataset were not included, the accuracy increased from 93.75% (95% CI 93.70%-93.80%) to 95.78% (95% CI 95.73%-95.82%), sensitivity increased from 65.00% (95% CI 64.33%-65.67%) to 70.13% (95% CI 68.98%-71.27%) and specificity remained unchanged at 98.49% (95% CI 98.45%-98.52%), respectively. The main causes of false negative results in older adults were patients’ lung markings, diaphragm or bowel airs (37%, n=14) followed by type I fracture (29%, n=11). The main causes of false negatives in younger adults were limbus-like VFs (45%, n=19), followed by type I fracture (26%, n=11). The overall kappa between AI discrimination and subspecialists’ consensus in the older and younger adult populations were 0.77 (95% CI, 0.733-0.805) and 0.72 (95% CI, 0.6524-0.80), respectively. CONCLUSIONS The developed VF-identifying AI ensemble model based on ground truth of older adults achieved better performance in identifying VFs in older adults and non-fractured thoracic and lumbar vertebrae in the younger adults. Different age distribution may have potential disease diversity and implicate the effect of ground truth generalizability on the AI model performance.

BACKGROUND This study aimed to compare the prediction performance of two-dimensional (2D) and three-dimensional (3D) semantic segmentation models for intracranial metastatic tumors with a volume ≥ 0.3 mL. METHODS We used postcontrast T1 whole-brain magnetic resonance (MR), which was collected from Taipei Veterans General Hospital (TVGH). Also, the study was approved by the institutional review board (IRB) of TVGH. The 2D image segmentation model does not fully use the spatial information between neighboring slices, whereas the 3D segmentation model does. We treated the U-Net as the basic model for 2D and 3D architectures. RESULTS For the prediction of intracranial metastatic tumors, the area under the curve (AUC) of the 3D model was 87.6% and that of the 2D model was 81.5%. CONCLUSION Building a semantic segmentation model based on 3D deep convolutional neural networks might be crucial to achieve a high detection rate in clinical applications for intracranial metastatic tumors.

In this paper, a novel, compact, And field-programmable gate array (FPGA)-based coating impedance detector (CID 2.0) was proposed to rapidly detect the early degradation of coatings. An FPGA-based hardware design with an embedded analog-to-digital converter and digital-to-analog converter was successfully applied to develop CID 2.0. A method for generating high-quality signals for FPGA by using the delta-sigma modulation was used. This method provided higher measurement ranges and accuracies for detecting coating impedance values. The performance of CID 2.0 was compared with that of a conventional potentiostat. For comparison, the impedance value of ideal resistors and commercial coatings was measured using the proposed and conventional methods. The results indicated an optimal correlation between the impedance values measured using CID 2.0 and that measured using conventional potentiostat in the range of 106 - 1010 Ω-cm2. Furthermore, when the continuous monitoring experiments were conducted, CID 2.0 exhibited high sensitivity to detect impedance changes associated with the coating delamination process. The preliminary results suggested that CID 2.0 can be used to observe the changes in the coating impedance values in the critical range to enable workers to determine whether coating maintenance should be scheduled.

The extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging (MRI) plays an important role in diagnosis before the gamma knife radiosurgery (GKRS). In this article, the post-contrast T1 whole-brain MRI images had been collected by Taipei Veterans General Hospital (TVGH) and stored in DICOM format (dated from 1999 to 2018). The proposed method starts with the active contour model to get the region of interest (ROI) automatically and enhance the image contrast. The segmentation models are trained by MRI images with tumors to avoid imbalanced data problem under model construction. In order to achieve this objective, a two-step ensemble approach is used to establish such diagnosis, first, classify whether there is any tumor in the image, and second, segment the intracranial metastatic tumors by ensemble neural networks based on 2D U-Net architecture. The ensemble for classification and segmentation simultaneously also improves segmentation accuracy. The result of classification achieves a F1-measure of $$75.64\%$$ 75.64 % , while the result of segmentation achieves an IoU of $$84.83\%$$ 84.83 % and a DICE score of $$86.21\%$$ 86.21 % . Significantly reduce the time for manual labeling from 30 min to 18 s per patient.

BACKGROUND: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported. QUESTIONS/PURPOSES: (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients’ clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation? METHODS: Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model. RESULTS: The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively. CONCLUSION: The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model. LEVEL OF EVIDENCE: Level II, diagnostic study.

This investigation studies the nonemptiness problems of Wang cubes with two colors. Wang cubes are unit cubes with colored faces, which are generalized from Wang tiles. For a set $B$ of Wang cubs, $\Sigma(B)$ is the set of all global patterns on $\mathbb{Z}^3$ that can be constructed by the cubes in $B$. The nonemptiness problem is to determine whether $\Sigma(B) \neq \emptyset$ or not. Denote by $\mathcal{P}(B)$ the set of all periodic patterns on $\mathbb{Z}^3$ that can be constructed by the cubes in $B$. For Wang cubes, the corresponding Wang's conjecture is that if $\Sigma(B) \neq \emptyset$, then $\mathcal{P}(B) \neq \emptyset$. ¶ We introduce the transition matrices and trace operators to determine whether $\Sigma(B) \neq \emptyset$ and $\mathcal{P}(B) \neq \emptyset$ or not, respectively. A basic set $B$ is called a minimal cycle generator if $\mathcal{P}(B) \neq \emptyset$ but $\mathcal{P}(B') = \emptyset$ for all $B' \subsetneqq B$. By computer computation, there exist $86$ equivalence classes of minimal cycle generators with two colors. By verifying that the basic sets $B$ that contains no minimal cycle generators satisfy $\Sigma(B) = \emptyset$, we prove that the Wang's conjecture for Wang cubes with two colors is true.

Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches: first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network. Our model demonstrated a good distinguishing ability for these common lung problems in children. Among the three methods, the one-versus-one scheme has the best performance. We could detect whether a chest X-ray image is abnormal with 92.47% accuracy and bronchiolitis/bronchitis, bronchopneumonia, lobar pneumonia, pneumothorax, or normal with 71.94%, 72.19%, 85.42%, 85.71%, and 80.00% accuracy, respectively. In conclusion, we provide a computer-aided diagnostic scheme by deep learning for common pulmonary diseases in children. This scheme is mostly useful as a screening for normal versus most of lower respiratory problems in children. It can also help review the chest X-ray images interpreted by clinicians and may remind possible negligence. This system can be a good diagnostic assistance under limited medical resources.

Background Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create three-dimensional (3D) geometries. Methods Five hundred eighteen patients who underwent procedures for circumferential isolation of four pulmonary veins were enrolled. Cardiac CT images (from 97 patients) were used to construct the LA detection and segmentation models. These images were reviewed by the cardiologists such that images containing the LA were identified/segmented as the ground truth for model training. Two DCNNs which incorporated transfer learning with the architectures of ResNet50/U-Net were trained for image-based LA classification/segmentation. The LA geometry created by the deep learning model was correlated to the outcomes of AF ablation. Results The LA detection model achieved an overall 99.0% prediction accuracy, as well as a sensitivity of 99.3% and a specificity of 98.7%. Moreover, the LA segmentation model achieved an intersection over union of 91.42%. The estimated mean LA volume of all the 518 patients studied herein with the deep learning model was 123.3 ± 40.4 ml. The greatest area under the curve with a LA volume of 139 ml yielded a positive predictive value of 85.5% without detectable AF episodes over a period of one year following ablation. Conclusions The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry.

In instalment of pyranometers at the weather stations for measuring global solar radiation, it often cannot avoid appearance of obstacles in their surroundings. Investigation of sheltering effect on measurement of global solar radiation is performed through introducing the shelter view factor. Evaluation of the shelter view factor is made by means of a fisheye-lens photograph together with the calculation method developed by Steyn (1980). Taitung weather station (TWS) is chosen for the study. The shelter view factor for the pyranometer installed at TWS is estimated to be 11.8%. Measurements of global solar radiation are conducted at a place where is located nearby TSW but with the condition of zero shelter view factor. Comparison of the data measured at these two sites indicates 4% - 25% deviations of global solar radiation observed in different months of 2017. It suggests a need of the correction to the sheltering effect in the measuring process of global solar radiation at the weather stations except those who are not subject to surrounding obstacles in the sky dome.