Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur’s Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.

Association of the Posterior Acromion Extension with Glenoid Retroversion: A CT Study in Normal and Osteoarthritic Shoulders

Posterior eccentric glenoid wear is associated with higher complication rates after shoulder arthroplasty. The recently reported association between the acromion shape and glenoid retroversion in both normal and osteoarthritic shoulders remains controversial. The three-dimensional coordinates of the angulus acromialis (AA) and acromioclavicular joint were examined in the scapular coordinate system. Four acromion angles were defined from these two acromion landmarks: the acromion posterior angle (APA), acromion tilt angle (ATA), acromion length angle (ALA), and acromion axial tilt angle (AXA). Shoulder computed tomography scans of 112 normal scapulae and 125 patients with primary glenohumeral osteoarthritis were analyzed with simple and stepwise multiple linear regressions between all morphological acromion parameters and glenoid retroversion. In normal scapulae, the glenoid retroversion angle was most strongly correlated with the posterior extension of the AA (R2 = 0.48, p < 0.0001), which can be conveniently characterized by the APA. Combining the APA with the ALA and ATA helped slightly improve the correlation (R2 = 0.55, p < 0.0001), but adding the AXA did not. In osteoarthritic scapulae, a critical APA > 15 degrees was found to best identify glenoids with a critical retroversion angle > 8 degrees. The APA is more strongly associated with the glenoid retroversion angle in normal than primary osteoarthritic scapulae.

Beneficial effects of the fructus Sophorae extract on experimentally induced osteoporosis in New Zealand white rabbits

Sophora japonica is a source of several flavonol, flavone and isoflavone glycosides that are reported to positively affect menopausal symptoms including osteoporotic complications. In the present study fructus Sophorae extract (FSE) was administered orally for three months at a dose of 200 mg kg–1 in ovariectomized (OVX) New Zealand rabbits. 3D computed tomography scans and histopathological images revealed microstructural disturbances in the bones of the castrated animals. FSE recovered most of the affected parameters in bones in a manner similar to zoledronic acid (ZA) used as a positive control. The aglycones of the main active compounds of FSE, daidzin, and genistin, were docked into the alpha and beta estrogen receptors and stable complexes were found. The findings of this study provide an insight into the effects of FSE on bone tissue loss and suggest that it could be further developed as a potential candidate for the prevention of postmenopausal osteoporotic complications.

The Role of the Thoracic Spine during Breathing in Osteogenesis Imperfecta: A Combined Traditional Morphometry and 3D Geometric Morphometrics Research

OsteogenesisImperfecta (OI) is a rare disease with respiratory problems, which are usually attributed to the secondary effects of scoliosis and rib fractures and to severe restrictive pulmonary disease. Conventional morphometry has already been studied in OI patients but three-dimensional geometric morphometrics (3D GMM) has never been used to assess how the thoracic spine shape changes during maximal breathing. A total of 6 adult subjects with OI type III and 16 healthy controls underwent a spirometric study and two computed tomography scans in maximal inspiration and expiration. Shape data by means of 3D GMM and Cobb angle values of scoliosis and kyphosis were obtained and their relationship with spirometric values was analysed using regressions and mean shape comparisons. No differences in kyphosis (p = 0.285) and scoliosis Cobb values (p = 0.407) were found between inspiration and expiration in OI patients. The 3D GMM analysis revealed significant shape differences between OI and control subjects (p < 0.001) that were related to the inspiration (p = 0.030) and not to the expiration (p = 0.079). Nevertheless, no significant relation was found between thoracic spine shape, scoliosis, kyphosis and breathing outcomes in both OI patients and controls. There were thoracic spine shape differences during maximal breathing between OI patients and controls that were mainly related to the inspiration.

Hybrid Convolutional Neural Network-Based Diagnosis System for Intracranial Hemorrhage

Early diagnosis of intracranial hemorrhage significantly reduces mortality. Hemorrhage is diagnosed by using various imaging methods and the most time-efficient one among them is computed tomography (CT). However, it is clear that accurate CT scans requires time, diligence, and experience. Computer-aided design methods are vital for the treatment because they facilitate early diagnosis of intracranial hemorrhage. At this point, deep learning can provide effective outcomes through an automated diagnosis way. However, as different from the known solutions, diagnosis of five different hemorrhage subtypes is a critical problem to be solved.This study focused on deep learning methods and employed cranial computed tomography scans in order to detect intracranial hemorrhage. The diagnosis approach in the study aimed to detect five subtypes of hemorrhage. In detail, EfficientNet-B3 and ResNet-Inception-V2 architectures were used for diagnosis purposes. Eventually, the study also proposed a two-architecture hybrid method for the diagnosis purpose. The obtained findings by the hybrid method were evaluated in terms of a comparative perspective.Results showed that the newly designed hybrid method was quite effective in terms of increasing classification rates of detecting intracranial hemorrhage according to the subtypes. Briefly, an accuracy of 98.5%, which is higher than those of the EfficientNet-B3 and the Inception-ResNet-V2, were obtained thanks to the developed hybrid method.

Deep Learning Applied to Intracranial Hemorrhage Detection

Intracranial hemorrhage is a serious health problem requiring rapid and often intensive medical care. Identifying the location and type of any hemorrhage present is a critical step in treating the patient. Diagnosis requires an urgent procedure and the detection of the hemorrhage is a hard and time-consuming process for human experts. In this paper, we propose a novel method based on Deep Learning techniques which can be useful as decision support system. Our proposal is two-folded. On the one hand, the proposed technique classifies slices of computed tomography scans for hemorrhage existence or not, achieving 92.7% accuracy and 0.978 ROC-AUC. On the other hand, our method provides visual explanation to the chosen classification by using the so-called Grad-CAM method. TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back TRANSLATE with x English ArabicHebrewPolish BulgarianHindiPortuguese CatalanHmong DawRomanian Chinese SimplifiedHungarianRussian Chinese TraditionalIndonesianSlovak CzechItalianSlovenian DanishJapaneseSpanish DutchKlingonSwedish EnglishKoreanThai EstonianLatvianTurkish FinnishLithuanianUkrainian FrenchMalayUrdu GermanMalteseVietnamese GreekNorwegianWelsh Haitian CreolePersian TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back