Scan Images Research Articles

Familial cross-testicular ectopia: a clinical case report

Cross or transverse testicular ectopia is a rare congenital condition of the reproductive system that involves the migration of a testicle into the opposite inguinal canal, along with the presence of an inguinal hernia on the same side as the ectopic testicle. In the modern literature, there are discussions about diagnostic issues, particularly the use of ultrasound scanning and magnetic resonance imaging to diagnose this condition. However, it is the results of laparoscopic examination that determine the most accurate criteria for surgical treatment. Many publications discuss the choice of surgical tactics, including separate transabdominal orchiopexy, with or without laparoscopic assistance; transeptal orchiopexy on both sides of the scrotum; and rare cases of orchiopexy for both testicles on one side of the scrotum. Ultimately, the choice of surgery depends on the individual characteristics and needs of the patient, and the surgeon should make the decision based on these factors. The article describes, for the first time, a case of familial testicular ectopia with bilateral inguinal hernia in siblings who underwent surgery using laparoscopic assistance. The method used was single-trocar laparoscopic access with transscrotal transeptal orchiopexy and simultaneous bilateral puncture of the inguinal ring and suture. During the long-term follow-up period (50 and 20 months after surgery) in both siblings, there were no signs of malformation or atrophy of the gonads.

Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease.

Due to similar clinical manifestations and imaging signs, differential diagnosis of primary intestinal lymphoma (PIL) and Crohn's disease (CD) is a challenge in clinical practice. To investigate the ability of radiomics combined with machine learning methods to differentiate PIL from CD. We collected contrast-enhanced computed tomography (CECT) and clinical data from 120 patients form center 1. A total of 944 features were extracted single-phase images of CECT scans. Using the last absolute shrinkage and selection operator model, the best predictive radiographic features and clinical indications were screened. Data from 54 patients were collected at center 2 as an external validation set to verify the robustness of the model. The area under the receiver operating characteristic curve, accuracy, sensitivity and specificity were used for evaluation. A total of five machine learning models were built to distinguish PIL from CD. Based on the results from the test group, most models performed well with a large area under the curve (AUC) (> 0.850) and high accuracy (> 0.900). The combined clinical and radiomics model (AUC = 1.000, accuracy = 1.000) was the best model among all models. Based on machine learning, a model combining clinical data with radiologic features was constructed that can effectively differentiate PIL from CD.

Distraction force promotes the osteogenic differentiation of Gli1+ cells in facial sutures via primary cilia-mediated Hedgehog signaling pathway

BackgroundTrans-sutural distraction osteogenesis (TSDO) involves the application of distraction force to facial sutures to stimulate osteogenesis. Gli1+ cells in the cranial sutures play an important role in bone growth. However, whether Gli1+ cells in facial sutures differentiate into bone under distraction force is unknown.Methods4-week-old Gli1ER/Td and C57BL/6 mice were used to establish a TSDO model to explore osteogenesis of zygomaticomaxillary sutures. A Gli1+ cell lineage tracing model was used to observe the distribution of Gli1+ cells and explore the role of Gli1+ cells in facial bone remodeling.ResultsDistraction force promoted bone remodeling during TSDO. Fluorescence and two-photon scanning images revealed the distribution of Gli1+ cells. Under distraction force, Gli1-lineage cells proliferated significantly and co-localized with Runx2+ cells. Hedgehog signaling was upregulated in Gli1+ cells. Inhibition of Hedgehog signaling suppresses the proliferation and osteogenesis of Gli1+ cells induced by distraction force. Subsequently, the stem cell characteristics of Gli1+ cells were identified. Cell-stretching experiments verified that mechanical force promoted the osteogenic differentiation of Gli1+ cells through Hh signaling. Furthermore, immunofluorescence staining and RT-qPCR experiments demonstrated that the primary cilia in Gli1+ cells exhibit Hedgehog-independent mechanosensitivity, which was required for the osteogenic differentiation induced by mechanical force.ConclusionsOur study indicates that the primary cilia of Gli1+ cells sense mechanical stimuli, mediate Hedgehog signaling activation, and promote the osteogenic differentiation of Gli1+ cells in zygomaticomaxillary sutures.

Sex estimation from mandibular morphometry using discriminant analysis–CT scan based retrospective study

It is well known that skeletal characteristics vary among different populations. Sex dimorphic characteristics of the mandible have been reported from studies using either mandible bone or computer tomography (CT) scan images of the mandible to test its efficacy in estimating sex. Mandibular studies for estimating sex in any particular population group may not hold well for different populations owing to inherent geographical variations. With this background, we assessed the validity of mandible measurements from CT scan images to determine sex in the South Indian population. As a part of our study, 11 metric parameters and 1 angular parameter were measured and analyzed. The mean values of all the parameters were significantly higher for males as compared to females. Bigonial breadth indicated the best discriminatory ability of the study. Mandibular angle had the least predictive accuracy among all the discriminant functions studied. We further observed that a combination of parameters gave the best overall classification rate (92.3 %). When the same functions were tested on an independent testing data set, a combination of parameters and bigonial breadth gave the best classification rate (82.1 %) which was consistent. We concluded that the mandible has a high accuracy for sex estimation. All the parameters studied demonstrated statistically significant differences between the two sexes except the mandibular angle.

Classification of Malignancy of Lung Cancer Using Backpropagation Algorithm on CT-Scan Images

In this study, we investigate the classification of lung cancer CT scan images based on malignancy level using a backpropagation artificial neural network (ANN). Lung cancer is a deadly disease characterized by the growth of abnormal lung cells. The proposed method involves preprocessing to enhance image quality, followed by feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method with angle variations of 0°, 45°, 90°, 135°, and d=1. The extracted features include energy, contrast, correlation, and homogeneity. The energy value range in malignant cancer is 0.27 to 0.81, while in benign cancer it is 0.26 to 0.73. The contrast in benign cancer ranges from 1.38 to 11.87, while in malignant cancer it is 1.47 to 13.67. The image correlation for malignant cancer is between 0.63 to 0.94, while for benign cancer it is 0.69 to 0.96. Homogeneity in malignant cancer has a value range between 0.67 to 0.91, while in benign cancer it ranges from 0.70 to 0.92. The classification of lung cancer malignancy is restricted to benign and malignant levels using a network architecture of [4 10 2], maximum iteration of 100000, and learning rate of 0.001. The accuracy of the testing data from the ANN is between 90% and 100%. These results demonstrate the effectiveness of the GLCM method and backpropagation algorithm in accurately classifying the malignancy level of lung cancer, which could aid in the early detection and treatment of the disease.

356 The Role of Cancer Cachexia in Influencing Outcomes in Pancreatic & Biliary Malignancy

Abstract Aim To investigate the influence of cancer cachexia on outcomes and clinicopathological variables in pancreatico-biliary malignancies. Method Patients who underwent pancreaticoduodenectomy for malignancy from 2007-2020 at the Glasgow Royal Infirmary were included in this study. Pre-operative single-slice CT scan images were collected for cross sectional analysis of body composition using the semi-automated segmentation programme, SliceOmatic. Body composition measurements were represented as z-scores to account for age and sex. Results Subcutaneous fat radiodensity (SFD) was significantly lower in ampullary adenocarcinoma (-0.17+/-0.95), duodenal adenocarcinoma (-0.03+/-1.08), and cholangiocarcinoma (-0.18+/-1.00) groups compared to the pancreatic cancer group (0.12+/-1.00) (P=0.09). Skeletal muscle index (SMI) was significantly higher in those diagnosed with cholangiocarcinoma compared to all other indications (0.20+/-0.91 vs -0.04+/-1.02, P=0.05). Disease-specific and recurrence-free median survival was lower in those with low SMI compared to those with high SMI for all indications (35 months vs 41 months, 13 months vs 18 months).When comparing those who underwent up-front resection to those who received neoadjuvant therapy, visceral fat index (VFI) was found to be higher (0.06+/-1.00 vs -0.21+/-0.99, P=0.03) and skeletal muscle density was found to be lower (-0.05+/-1.01 vs 0.18+/-0.96, P=0.04). SFD was also found to be significantly lower in those with AJCC stage 1 compared to all other stages (-0.21+/-0.86 vs 0.07+/-1.01, P=0.03). Conclusions Adverse body composition may influence outcomes in pancreatic and biliary malignancy; however, this influence appears to be modest at best. Clinical applications of body composition analysis may be limited to serial monitoring of patients within the context of their treatment pathway.

A feasibility study on scanning imaging of tip-enhanced Raman spectroscopy using a spiral plasmonic lens

A feasibility study on scanning imaging of tip-enhanced Raman spectroscopy using a spiral plasmonic lens

A method for calculating vector forces at human-mattress interface during sleeping positions utilizing image registration

The vector forces at the human-mattress interface are not only crucial for understanding the distribution of vertical and shear forces exerted on the human body during sleep but also serves as a significant input for biomechanical models of sleeping positions, whose accuracy determines the credibility of predicting musculoskeletal system loads. In this study, we introduce a novel method for calculating the interface vector forces. By recording indentations after supine and lateral positions using a vacuum mattress and 3D scanner, we utilize image registration techniques to align body pressure distribution with the mattress deformation scanning images, thereby calculating the vector force values for each unit area (36.25 mm × 36.25 mm). This method was validated through five participants attendance from two perspectives, revealing that (1) the mean summation of the vertical force components is 98.67% ± 7.21% body weight, exhibiting good consistency, and mean ratio of horizontal component force to body weight is 2.18% ± 1.77%. (2) the predicted muscle activity using the vector forces as input to the sleep position model aligns with the measured muscle activity (%MVC), with correlation coefficient over 0.7. The proposed method contributes to the vector force distribution understanding and the analysis of musculoskeletal loads during sleep, providing valuable insights for mattress design and evaluation.

Retrieval of upper stratospheric ozone profiles from SCIAMACHY Hartley-Huggins limb scatter spectra using WMART

ABSTRACT The hyperspectral SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument, launched by Envisat in March 2002, measures atmospheric scattered radiance within the ultraviolet (UV) to near infrared range and has three geometric observation modes: nadir, occultation, and limb. This study describes a retrieval scheme to obtain middle and upper stratospheric ozone profiles from SCIAMACHY limb-scattered solar radiance. Owing to the low ozone concentration at high altitudes and rapid changes in ozone absorption cross-sections in the Hartley – Huggins band, a fundamental objective of this study was to establish a simple and effective retrieval scheme to derive reliable ozone profiles at high altitudes from UV spectral bands. In this study, short UV wavelengths of 302, 312, and 322 nm and a long wavelength of 331 nm were used for wavelength pairing to simplify the retrieval algorithm. Moreover, we employed the weighted multiplicative algebraic reconstruction technique (WMART), which is straightforward to understand, and easy to implement, combined with a radiative transfer model (i.e. SCIATRAN), and produced stable results. The retrieval error estimates for the upper stratosphere were provided, and the total retrieval error of the ozone profile was usually < 18% in the altitude range of 30–53 km. The results were compared with the SCIAMACHY v3.5 product and validated against profiles derived from Optical Spectrometer and InfraRed Imaging System (OSIRIS) v7.1, and Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) v4.0 for 30–53 km altitude. The retrieved ozone profiles agreed with data from SCIAMACHY v3.5 within − 10%–5%, except at an altitude of 45 km, where the bias became larger (approximately −15%). The comparison of retrieved profiles with OSIRIS v7.1 showed differences within ± 10% for 30–53 km altitude. The errors were mostly less than − 5% below 43 km compared with ACE-FTS v4.0, but there was a large negative deviation in the upper stratosphere.

Unveiling Sex through Radiological Insights: Exploring the Potential Approach to Sex Estimation Using Hyoid Bone in Multi-detector Computed Tomography Scans

Abstract Background: The identification of the individual from the corpse remains is one of the most difficult tasks in forensic science. Multiple markers in the human body can be used to generate a profile of a person. Using computed tomography (CT) scan images, we are attempting to determine the relevance of the hyoid bone for sexual dimorphism. Materials and Methods: Using the axial reconstructed images, 10 different measurements were taken using a digital imaging and communications in medicine viewer and analyzed for significance. Results: The hyoid body height, hyoid body length, maximum length of greater cornua of hyoid bone of the right side, and width of the proximal end of greater cornua of the right side measurements show statistically significant differences between males and females. The majority of male measures were much larger than those of females and also shows right side is more significant than the left side. The present study shows 82.1% correct classification for males and 81.5% for females. Conclusions: The present study concluded that the estimation of sex by hyoid bone using a multidetector CT scan can be useful in determining the sex.

Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.

Role of 3D CT with Humeral Subtraction in Assessing Anteromedial Facet Coronoid Fractures

BackgroundThe anteromedial facet (AMF) of the coronoid is a key structure in resisting varus posteromedial rotatory instability (PMRI) of the elbow. However, not all isolated coronoid fractures involve the AMF and not all fractures involving the AMF are the result of a PMRI mechanism. There is debate regarding the management of isolated coronoid fractures. A reliable method of differentiating this heterogeneous group of isolated coronoid fractures is essential to develop an appropriate management algorithm. The aim of this study was to evaluate the role of additional humeral subtraction 3D images in the detailed assessment of the known radiographic features of AMF fractures with PMRI mechanism. MethodsThree upper extremity fellowship-trained orthopedic surgeons evaluated 32 consecutive CT scans in patients with isolated coronoid fractures, on two occasions separated by at least 5 months. On each occasion, CT scan images were evaluated for fracture morphology and orientation in two rounds. In the first round, the evaluation was made based on all 2D and 3D reconstruction images of the entire elbow; in the second round, the surgeons had access to images from the first round plus 3D reconstruction with humeral subtraction. Statistical analysis to assess agreement amongst the surgeons was performed using the kappa multi-rater analysis. Intraobserver agreement was evaluated using Pearson’s correlation coefficient. ResultsThe addition of the humeral subtraction view significantly improved the interobserver agreement for fracture morphology from 0.28 (95% CI 0.07-0.49) to 0.66 (95% CI 0.46-0.87), p< 0.001; and for orientation from 0.31 (95% CI 0.09-0.52) to 0.54 (95% CI 0.31-0.77), p<0.001. Similarly, the intraobserver Pearson correlation improved from 0.28-0.38 to 0.48-0.76 for fracture morphology, and from 0.36-0.77 to 0.51-0.69 for fracture orientation. Conclusion3D CT reconstruction with humeral subtraction improved surgeons’ ability to characterize radiographic features of AMF coronoid fractures. Future studies are required to determine whether better characterization of the morphology and orientation of AMF fractures allows for the categorization of these fractures into more homogenous groups and the development of more consistent management algorithms.

Novel evaluation of pulmonary hypertension associated with chronic lung disease using perfusion SPECT/CT: A pilot study.

In pulmonary hypertension (PH) associated with chronic lung disease (CLD), identifying patients who would benefit from pulmonary vasodilators is a significant clinical challenge because the presence of PH is associated with poorer survival. This study evaluated the severity of pulmonary circulation impairment in patients with CLD-PH using pulmonary perfusion single-photon emission computed tomography/computed tomography (SPECT/CT). This single-center, observational study enrolled patients with CLD-PH who had a mean pulmonary arterial pressure (PAP) ≥ 25 mmHg, as confirmed by right heart catheterization. The primary outcome was to measure the percentage of pulmonary perfusion defect (%PPD), calculated by dividing the perfusion defect volume from perfusion SPECT images by the lung volume from CT scan images. The secondary outcome was to assess the correlation between %PPD and baseline characteristics. The median %PPD was 52.4% (interquartile range, 42.5%-72.3%) in 22 patients. In multivariate linear regression analysis, both forced vital capacity (β = 0.58, p = 0.008) and mean PAP (β = 0.68, p = 0.001) were significantly correlated with %PPD. In conclusion, significant correlation between mean PAP and %PPD in patients with CLD-PH was observed. This noninvasive assessment of %PPD may be useful for evaluating the severity of pulmonary circulation impairment in CLD-PH.

Lung Cancer Detection and Classification Using CNN

Abstract: Recognized for its severity and widespread impact, cancer remains a leading cause of death worldwide, claiming countless lives annually. Touching upon cancer genres, pleura tumor tops the list as the most widespread and deadliest form. In the detection of pleura tumor, CT scans (computed topography) play a crucial role by offering a detailed view of tumor presence and progression. Although CT scans function as primary choice pleura tumor determination, the subjective nature of visual analysis can introduce inaccuracies and obstacles. Thus, the use of image processing methods has become prevalent in medaical research, particularly for the early determination of pleura tumor. This paper presents a new methodology for determine pleura cancer from given inputs. This study presents an algorithm aimed at detecting pleura tumor, which incorporates methods such as median filtering to preprocess images and a set of techniques used in image processing and computer vision for analyzing and manipulating shapes within images are employed to divide the bronchial area of focus. Geometrical features derived from this segmented area are then utilized in a support vector machine classifier to discern between healthy and peculiar CT scan images

A Systematic Literature Review: Performance Comparison of Edge Detection Operators in Medical Images

Medical images play a crucial role in the diagnosis of diseases. To make the diagnosis more accurate, the image should usually be enhanced first using image processing methods such as segmentation and edge detection stages. However, the complexity and noise that may arise in these images pose challenges in edge detection. Therefore, to portray the characteristics of edge detection operators, this research presents a systematic literature review of the performance of various edge detection operators in medical images, focusing on literature published between 2019 and 2023. After the selection process, 41 papers out of the initial 112 collected papers were chosen for further review. The study evaluates edge detection operators e.g., Canny, Sobel, Prewitt, Roberts, and Laplacian of Gaussian (LOG) on medical images such as X-rays, MRI, CT scans, ultrasound, Pap smears, and others. In the analysis, the accuracy, computational time, and response to noise of each operator are compared. The results indicate that despite longer computational times, Canny emerges as the most accurate operator, especially in Pap smear and CT scan images. The LOG operator offers high accuracy in MRI images with more efficient computational time. Evaluation of operator reliability against noise confirms the superiority of Canny. Furthermore, the future potential of edge detection in medical services is also reviewed. For instance, Canny, known for accurate and noise-resistant edges, enhances detection in complex CT-Scan and X-ray images. Meanwhile, LOG, handling artifacts with lower computational time, improves edge clarity in medical images. Potential applications include enhanced diagnosis, efficient patient monitoring, and improved image clarity in future medical services.

Development and Validation of a Diagnosis System for Lung Infection Using Hybrid Deep-Learning Techniques

A fast and accurate test is necessary to detect COVID-19. A computed tomography (CT) scan has shown diagnostic accuracy. CT scan processing using a deep learning architecture may improve illness diagnosis and treatment. We proposed a deep learning system for COVID-19 detection using CT images, including using and comparing transfer-learning, fine-tuning, and the embedding process. This paper presents the development of a COVID-19 case identification model using deep learning techniques. The suggested model utilized a modified visual geometry group (VGG16) architecture as the deep learning framework. The model was trained and validated using a chest CT image dataset. The SARS-COV-2 dataset contains 2482 CT scans of 210 patients from publicly available sources. The modified model demonstrated encouraging outcomes by greatly enhancing the sensitivity measure (95.82±1.75)%, which is an essential criterion for accurately detecting instances of COVID-19 infection. In addition, the model achieved higher values for the accuracy metric (91.67±1.68)%, the specificity meter (88.08±3.72)%, the precision metric (87.51±3.27)%, the F1 score (91.43±1.55)%, and the area under the curve (91.98±1.55)%. Deep learning effectively detects COVID-19 in chest CT scan images. Clinical practitioners may employ the suggested approach to study, identify, and effectively mitigate a greater number of pandemics.

Learning Application Model of Head CT Scan Image Evaluation for Health Human Resource Capacity Building

Learning Application Model of Head CT Scan Image Evaluation for Health Human Resource Capacity Building

Recurrent Hydatid Disease of Temporal Bone and Skull Base: A Case Report and Literature Review.

Hydatid disease is a parasitic infestation caused by Echinococcus granulosus. The disease is rare in the head and neck region with varying presentations. We present a rare case of recurrent hydatid disease of the temporal bone with intracranial involvement; and the treatment thereof. Hydatid disease is endemic in few parts of the world, its occurrence in head and neck region is very rare. The commonest causative organism is E. granulosus giving rise to cystic hydatid disease. Most of the former are asymptomatic, but can give rise to symptoms because of their mass effects or rupture. Our case is a 38year male presenting to ENT department with recurrent left ear discharge, giddiness, headache and facial palsy having operated elsewhere twice in the past. The patient was investigated by Computed Tomography (CT) scanning and Magnetic Resonance Imaging (MRI) of temporal bones, head and neck regions. Which showed soft tissue attenuation of middle ear cleft and multiple cystic lesions in the middle and posterior cranial fossae indicating intracranial extension of disease. We operated for left subtotal petrosectomy with vestibular labyrinthectomy. During the perioperative period he was treated with Albendazole. Patient remains clinically and radiologically asymptomatic (post surgery 1year) till date of this manuscript submission. Hence, we report the recurrent hydatid cyst in the temporal bone and skull base, which was managed by combination of surgical and medical treatment.

Damage evolution in asphalt mixtures based on in-situ CT scanning

A novel approach utilizing in-situ Computed Tomography (CT) scanning and digital image processing techniques was developed to overcome the limitations of traditional CT image analysis in studying microcrack evolution in asphalt mixtures. Two types of graded asphalt mixtures (continuous gradation/AC and intermittent gradation/SMA) were scanned in-situ to capture internal structural changes. Digital image processing distinguished primary voids from cracks and extracted crack evolution. Multivariate regression analysis linked crack parameters to compressive strain. The study found that crack evolution during uniaxial compression in asphalt mixtures can be divided into densification, linear elastic deformation, and nonlinear failure stages. As load increases, the number, volume, and void percentage occupied by cracks rise, peak, and then decrease due to merging and engulfing after the ultimate load. The skeletal structure of cracks was obtained, and a damage evolution equation was established, linking crack parameters to damage evolution. Crack tortuosity was used to characterize the resistance effect of different gradations on crack propagation. This methodology provides new insights and technical support for studying damage evolution and understanding the failure mechanisms of asphalt mixtures.

Optimization of the VGG Deep Learning Model Performance for Covid-19 Detection Using CT-Scan Images

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2) causes a pneumonia-like disease known as Coronavirus Disease 2019 (COVID-19). The Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is the current standard for detecting COVID-19. However, CT scans can be applied for radiological inspection to detect infections in their earliest lung stages. Machine learning, specifically deep learning, can potentially speed up the evaluation of CT scan diagnoses of COVID-19. To date, no studies have been discovered that employ SGD, Adamax, or AdaGrad optimization methods with deep learning VGG model variants for COVID-19 detection in CT scan images with datasets comprising 2,038 images. This study aims to assess and compare the performance of various optimization methods for detecting COVID-19 utilizing variations of the VGG-16 and VGG-19 models based on CT scan images. Results from performance optimization comparison tests employing two VGG deep learning models were obtained, demonstrating the influence of optimization methods on model performance. The Adamax optimization method applied to the VGG-16 model performance achieved an average accuracy of 94.11% in COVID-19 detection using CT scan images, while the Adamax optimization method applied to the VGG-19 model performance achieved an average accuracy of 93.77%.