Simultaneous Bilateral Femoral Neck Fractures in a Pediatric Patient Attributed to Epilepsy Convulsion: A Case Report.

Introduction: Bilateral femoral neck fractures are rare among children. Although several case reports have been published, fractures caused by epilepsy attacks in children have not been reported in the literature. This is the first report of simultaneous bilateral femoral neck fractures in a pediatric patient with epilepsy convulsions. Case Report: This is a case of a child with bilateral femoral neck fractures caused by epileptic seizures. A 13-year-old Japanese boy had an epileptic seizure and was admitted to our hospital. The patient complained of bilateral thigh pain. Plain radiography revealed a bilateral femoral neck fracture. Conclusion: The patient's simultaneous bilateral femoral neck fractures were successfully managed with closed reduction and internal fixation, a careful postoperative course, and ultrasound fracture therapy. Despite the delay in diagnosis, bone union was confirmed 6 months postoperatively. Pediatric bilateral femoral neck fractures without a history of trauma are rare and likely to be missed. This case was a teachable experience highlighting the importance of being vigilant about fractures in children with postepileptic seizures.

Visual and quantitative evaluation of microcalcifications in mammograms with deep learning-based super-resolution.

PURPOSE: To evaluate visually and quantitatively the performance of a deep-learning-based super-resolution (SR) model for microcalcifications in digital mammography. METHOD: Mammograms were consecutively collected from 5080 patients who underwent breast cancer screening from January 2015 to March 2017. Of these, 93 patients (136 breasts, mean age, 50 ± 7 years) had microcalcifications in their breasts on mammograms. We applied an artificial intelligence model known as a fast SR convolutional neural network to the mammograms. SR and original mammograms were visually evaluated by four breast radiologists using a 5-point scale (1: original mammograms are strongly preferred, 5: SR mammograms are strongly preferred) for the detection, diagnostic quality, contrast, sharpness, and noise of microcalcifications. Mammograms were quantitatively evaluated using a perception-based image-quality evaluator (PIQE). RESULTS: All radiologists rated the SR mammograms better than the original ones in terms of detection, diagnostic quality, contrast, and sharpness of microcalcifications. These ratings were significantly different according to the Wilcoxon signed-rank test (p <.001), while the noise score of the three radiologists was significantly lower (p <.001). According to PIQE, SR mammograms were rated better than the original mammograms, showing a significant difference by paired t-test (p <.001). CONCLUSION: An SR model based on deep learning can improve the visibility of microcalcifications in mammography and help detect and diagnose them in mammograms.

Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets.

OBJECTIVES: The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography. METHODS: Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model's sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets. RESULTS: The hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45-0.47 mFPI and had partial AUCs of 0.93 in both test datasets. CONCLUSIONS: The DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.

Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method.

We developed and validated a deep learning (DL)-based model using the segmentation method and assessed its ability to detect lung cancer on chest radiographs. Chest radiographs for use as a training dataset and a test dataset were collected separately from January 2006 to June 2018 at our hospital. The training dataset was used to train and validate the DL-based model with five-fold cross-validation. The model sensitivity and mean false positive indications per image (mFPI) were assessed with the independent test dataset. The training dataset included 629 radiographs with 652 nodules/masses and the test dataset included 151 radiographs with 159 nodules/masses. The DL-based model had a sensitivity of 0.73 with 0.13 mFPI in the test dataset. Sensitivity was lower in lung cancers that overlapped with blind spots such as pulmonary apices, pulmonary hila, chest wall, heart, and sub-diaphragmatic space (0.50-0.64) compared with those in non-overlapped locations (0.87). The dice coefficient for the 159 malignant lesions was on average 0.52. The DL-based model was able to detect lung cancers on chest radiographs, with low mFPI.

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose To train and to validate a deep learning (DL)-based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness. Materials and Methods A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 per patient. The development data set was divided into training and validation data sets at ratio of 3:1 per patient. The DL model was created by using the training data set, tuned with the validation data set, and then evaluated quantitatively with the external test data set and visually with the misregistration test data set. Quantitative evaluations used the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) with mixed liner models. Visual evaluation was conducted by using a numerical rating scale. Results The training, validation, nonmisregistration test, and misregistration test data sets included 10 751, 2784, 1346, and 711 paired images collected from 40 patients (mean age, 62 years ± 11 [standard deviation]; 33 women). In the quantitative evaluation, DL-generated angiograms showed a mean PSNR value of 40.2 dB ± 4.05 and a mean SSIM value of 0.97 ± 0.02, indicating high coincidence with the paired DSA images. In the visual evaluation, the median ratings of the DL-generated angiograms were similar to or better than those of the original DSA images for all 24 sequences. Conclusion The deep learning-based model provided clinically useful cerebral angiograms free from clinically significant artifacts directly from dynamic angiograms. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Visualizing "featureless" regions on mammograms classified as invasive ductal carcinomas by a deep learning algorithm: the promise of AI support in radiology.

PURPOSE: To demonstrate how artificial intelligence (AI) can expand radiologists' capacity, we visualized the features of invasive ductal carcinomas (IDCs) that our algorithm, developed and validated for basic pathological classification on mammograms, had focused on. MATERIALS AND METHODS: IDC datasets were built using mammograms from patients diagnosed with IDCs from January 2006 to December 2017. The developing dataset was used to train and validate a VGG-16 deep learning (DL) network. The true positives (TPs) and accuracy of the algorithm were externally evaluated using the test dataset. A visualization technique was applied to the algorithm to determine which malignant findings on mammograms were revealed. RESULTS: The datasets were split into a developing dataset (988 images) and a test dataset (131 images). The proposed algorithm diagnosed 62 TPs with an accuracy of 0.61-0.70. The visualization of features on the mammograms revealed that the tubule forming, solid, and scirrhous types of IDCs exhibited visible features on the surroundings, corners of the masses, and architectural distortions, respectively. CONCLUSION: We successfully showed that features isolated by a DL-based algorithm trained to classify IDCs were indeed those known to be associated with each pathology. Thus, using AI can expand the capacity of radiologists through the discovery of previously unknown findings.

Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From Mammograms.

PURPOSE: The molecular subtype of breast cancer is an important component of establishing the appropriate treatment strategy. In clinical practice, molecular subtypes are determined by receptor expressions. In this study, we developed a model using deep learning to determine receptor expressions from mammograms. METHODS: A developing data set and a test data set were generated from mammograms from the affected side of patients who were pathologically diagnosed with breast cancer from January 2006 through December 2016 and from January 2017 through December 2017, respectively. The developing data sets were used to train and validate the DL-based model with five-fold cross-validation for classifying expression of estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2-neu (HER2). The area under the curves (AUCs) for each receptor were evaluated with the independent test data set. RESULTS: The developing data set and the test data set included 1,448 images (997 ER-positive and 386 ER-negative, 641 PgR-positive and 695 PgR-negative, and 220 HER2-enriched and 1,109 non-HER2-enriched) and 225 images (176 ER-positive and 40 ER-negative, 101 PgR-positive and 117 PgR-negative, and 53 HER2-enriched and 165 non-HER2-enriched), respectively. The AUC of ER-positive or -negative in the test data set was 0.67 (0.58-0.76), the AUC of PgR-positive or -negative was 0.61 (0.53-0.68), and the AUC of HER2-enriched or non-HER2-enriched was 0.75 (0.68-0.82). CONCLUSION: The DL-based model effectively classified the receptor expressions from the mammograms. Applying the DL-based model to predict breast cancer classification with a noninvasive approach would have additive value to patients.

Enantioselective Synthesis of Fluoro-Dihydroquinazolones and -Benzooxazinones by Fluorination-Initiated Asymmetric Cyclization Reactions.

Stereoselective synthesis of two fluorine-bearing drug-like scaffolds, dihydroquinazolone and benzooxazinone, has been accomplished through asymmetric fluorocyclization reactions initiated by the fluorination process. The reaction employs double axially chiral anionic phase-transfer catalysts to achieve high diastereo- and enantioselectivities, and a wide range of fluorine-containing dihydroquinazolones were obtained (>20:1 dr, up to 98% ee).

Investigation of iodine deficient state and iodine supplementation in patients with severe motor and intellectual disabilities on long-term total enteral nutrition.

Iodine concentrations of enteral nutrition (EN) formulae available in Japan are very low and long-term total EN (TEN) might result in hypothyroidism due to iodine deficiency (HID). Our aim of this study was to determine the degree of iodine deficiency (ID) and need for iodine supplementation (IS) in patients with severe motor and intellectual disabilities (SMID) on long-term TEN. Thyroid function including urinary iodine concentration (UIC) was monitored, and powdered kelp was provided as a source of iodine supplement. Thirty-five SMID on TEN participated in our study. UIC less than 100 µg /L, representing ID, were detected in 97 % of them. Their TSH ranged from 0.5 to 90 µIU/mL. IS using powdered kelp raised their UIC to the normal range. Thyroid function also recovered in the five hypothyroidism cases, which were diagnosed as HID, was also detected. In Japan, there must be many cases with ID associated with long term TEN. We also discuss the regulation of thyroid function in the iodine deficient state.

Nonenzymatic enantioselective monoacetylation of prochiral 2-protectedamino-2-alkyl-1,3-propanediols utilizing a chiral sulfonamide-Zn complex catalyst.

[structure: see text] Treatment of a chiral sulfonamide with Et(2)Zn gave quantitatively its Zn complex and then the structure was determined by X-ray crystallographic analysis. Reaction of prochiral N-Boc-2-amino-2-alkyl-1,3-propanediols with Ac(2)O in the presence of 5 mol % of chiral sulfonamide-Zn complex catalyst afforded the corresponding chiral monoacetyl products in 70-92% yields with 70-88% ee values. The proposed mechanism for the catalytic monoacetylation of a prochiral 1,3-propanediol was presented on the basis of CSI-MS analysis.