Analysis Of Computed Tomography Scans Research Articles

Which type of supra-syndesmal fractures Weber C or Equivalent Weber C have the best reduction of the distal tibio-fibular syndesmosis? A prospective CT-scan investigation on 60 ankles

IntroductionThe reduction of the distal tibiofibular syndesmosis (SyTFd) is a crucial factor in the functional prognosis following a suprasyndesmotic ankle fracture. Few studies have compared, using computed tomography (CT), the quality of SyTFd reduction in Weber C ankle fractures (WebC) and Equivalent Weber C (EqWebC) fractures, where there is a medial ligament injury instead of a bony malleolar lesion. This CT-based study aimed to answer the following questions: (1) Do Weber C fractures allow for better reduction of the distal tibiofibular syndesmosis compared to Equivalent Weber C fractures? (2) Are the functional outcomes one year postoperatively better for Weber C fractures compared to Equivalent Weber C fractures? HypothesisThe hypothesis was that WebC fractures would present better reduction of the SyTFd than EqWebC fractures due to the restoration of bone anatomy, considering the bony nature of the medial ligament involvement. Materials and methodsSince December 2021, all patients presenting with an ankle fracture were included in a database. Suprasyndesmotic fractures were extracted, and between December 2021 and February 2022, 60 patients underwent surgery for a suprasyndesmotic fracture (28 WebC – 32 EqWebC). All patients were operated on using the same technique and underwent a postoperative bilateral CT scan in axial slices with both ankles in a neutral position (foot at 90 ° to the leg). An analysis of 8 measurements was performed under the same conditions. Each fractured ankle was compared to the healthy contralateral ankle, and a delta was obtained for each measurement. The follow-up was standardized. At one year, an AOFAS Score and a Maryland Foot Score (MFS) were recorded. Statistical differences between the two groups were measured using the Student's t-test and Chi-square test. ResultsThere was a significant difference between the WebC and EqWebC groups for clinical scores at one year postoperatively: AOFAS: 92.0 ± 6.3 [78–100] vs 80.1 ± 5.4 [62–100], p < 0.05; MFS: 90.9 ± 6.4 [78–100] vs 81.6 ± 5.2 [64–100], p < 0.05. CT scan analysis of the SyTFd reduction found significantly better reduction in the WebC group. DiscussionWebC fractures showed better clinical scores associated with better SyTFd reduction on postoperative CT scans. The anatomical reduction related to the bone reduction criteria during surgery could explain our findings. Level of evidenceIII; Case-Control Comparative Study.

Complicated calcified alloplastic implants in the nasal dorsum: A clinical analysis.

In rhinoplasty, calcification around silicone implants is frequently observed in the tip dorsum (TD) area. Additionally, based on a review of various literature, it is presumed that calcification in silicone implants occurs due to both inflammatory chemical reactions and physical friction against the tissue. The calcification of nasal silicone implants not only results in the functional loss of the implants, but also leads to material deformation. However, there is a lack of research on calcification of nasal silicone implants in the current literature. To elucidate various clinical characteristics of calcification around nasal silicone implants, using histological and radiological analysis. This study analyzed data from 16 patients of calcified nasal implants, who underwent revision rhinoplasty for various reasons after undergoing augmentation rhinoplasty with silicone implants. The collected data included information on implant duration, implant types, location of calcification, presence of inflammatory reactions, and computed tomography (CT) scans. The most common location of calcification, as visually analyzed, was in the TD area, accounting for 56%. Additionally, the analysis of CT scans revealed a trend of increasing Hounsfield Unit values for calcification with the duration of implantation, although this trend was not statistically significant (P = 0.139). Our study shows that reducing the frequency of calcification may be achievable by using softer silicone implants and by minimizing the damage to perioperative tissues.

Anterior Ethmoid Roof Configuration: A Computed Tomography Scan Analysis of Adult Nigerians with Chronic Rhinosinusitis

Abstract Context: The anterior skull base has been adjudged a high-risk area in endoscopic sinus and skull base surgery. Hence, an understanding of its complex anatomy is crucial if catastrophic intra-operative complications must be avoided. Aims: To identify the anatomical configurations of the anterior skull base by radiological analysis, and to determine the prevalence of its variations among adults with chronic rhinosinusitis. Settings and Design: A retrospective study of 99 adult patients with chronic rhinosinusitis who attended the Ear Nose and Throat clinic of Aminu Kano Teaching Hospital from July 2022 to June 2023. Methods and Materials: The patient’s records were retrieved, and the basic demographic data, clinical history, and examination findings were extracted. All patients had diagnostic nasal endoscopy, and computed tomography scans of the nose and paranasal sinuses which were analyzed using a computer software program. Statistical Analysis Used: The data was analyzed with SPSS version 22.0 using frequency, mean, Pearson Chi-square tests and the results were presented in tables and figures. Results: A total of 99 patients’ CT scans were analyzed. There were 61 males (61.6%) and 38 females 38.4%) with a mean age of 31.7 years. 37 (37.4%) and 62 (62.6%) of patients had asymmetry and symmetry in the fovea ethmoidalis respectively. The anterior ethmoid artery was found coursing through the anterior ethmoidal air cells in 73 (36.9%) and along the anterior skull base in 125 patients (63.1%). Kero’s classification showed types I in 140 (70.7%), type II in 54 (27.3%) and type III in 4 (2.0%). Conclusion: This study found that a significant proportion of the study population had ethmoid roof asymmetry and the anterior ethmoid artery was found coursing through the anterior ethmoidal air cells respectively.

Uncommon Causes of Interlobular Septal Thickening on CT Images and Their Distinguishing Features.

Interlobular septa thickening (ILST) is a common and easily recognized feature on computed tomography (CT) images in many lung disorders. ILST thickening can be smooth (most common), nodular, or irregular. Smooth ILST can be seen in pulmonary edema, pulmonary alveolar proteinosis, and lymphangitic spread of tumors. Nodular ILST can be seen in the lymphangitic spread of tumors, sarcoidosis, and silicosis. Irregular ILST is a finding suggestive of interstitial fibrosis, which is a common finding in fibrotic lung diseases, including sarcoidosis and usual interstitial pneumonia. Pulmonary edema and lymphangitic spread of tumors are the commonly encountered causes of ILST. It is important to narrow down the differential diagnosis as much as possible by assessing the appearance and distribution of ILST, as well as other pulmonary and extrapulmonary findings. This review will focus on the CT characterization of the secondary pulmonary lobule and ILST. Various uncommon causes of ILST will be discussed, including infections, interstitial pneumonia, depositional/infiltrative conditions, inhalational disorders, malignancies, congenital/inherited conditions, and iatrogenic causes. Awareness of the imaging appearance and various causes of ILST allows for a systematic approach, which is important for a timely diagnosis. This study highlights the importance of a structured approach to CT scan analysis that considers ILST characteristics, associated findings, and differential diagnostic considerations to facilitate accurate diagnoses.

Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery

Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients. To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery. This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. Body composition derived from automated analysis of multislice abdominal CT scans. The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program. The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS. In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

Look for the POLESTAR (POsteroLateral Engagement of Soft Tissue And Radial head) while navigating around elbow dislocation

IntroductionElbow dislocation is frequently associated with bony and osteochondral postero-lateral capitellar lesions that are often under-reported. We aim to examine the radiological signs of postero-lateral lesions on Computer Tomography (CT) . MethodsA retrospective analysis of CT scans was performed. Patients were classified into 4 groups: 1) simple elbow dislocation, 2) elbow dislocation with a fracture of the coronoid tip, 3) elbow dislocation with coronoid tip fracture and a radial head fracture inferior or equal to the anterior third, 4) terrible triad defined as elbow dislocation with concomitant coronoid and a radial head fracture with a more important involvement. Patients with a more complex fracture pattern were excluded. The presence of POstero Lateral Engagement of Soft Tissue And the Radial head (POLESTAR) patterns was analyzed and subclassified as impaction type or fragmentation type. Results51 CT scans met the inclusion criteria. POLESTAR lesions were identified in 48 cases (94%): 46% impaction type and 54% fragmentation type POLESTAR. Analyzing patients from grade 1 to 4, impaction type POLESTAR was found respectively from 40% to 57%, while fragmentation type was present from 60% to 43%. ConclusionsThis study shows a high incidence of POLESTAR lesions (94%) that can be present as impaction type or fragmentation type. Based on our preliminary results, impaction type is more common in Grades 3 and 4, while fragmentation type is more frequent in grades 1 or 2.

Enhancing Hydatid Cyst Classification with Deep Learning and Convolutional Neural Networks Using CT Scans

Background: Hydatid cysts, caused by Echinococcus granulosis, are a serious health concern with potential complications. Traditional diagnostic methods, like clinical examination and imaging interpretation, can be subjective and error-prone. Artificial Intelligence and Deep Learning techniques can revolutionize healthcare by enhancing disease detection and diagnosis, with the study focusing on precise detection and classification. Methods: A Convolutional Neural Network (CNN) model was developed, utilizing image preprocessing techniques to accurately classify hydatid cysts in Computed Tomography (CT) scans. Training relied on a curated dataset, enabling the model to learn and identify key patterns indicative of hydatid cyst presence and its stage detection in CT scan images. Result: The AI model employed in this study achieved a 90% accuracy in classifying hydatid cyst stages using CT scan images. By providing essential information about the cyst stage, healthcare professionals can accurately inform patients based on CT scan analysis. Conclusion: The study explores the use of AI and DL in hydatid cyst stage classification using a CNN model trained on CT scan images. The approach aims to reduce hydatid cyst growth rates by aiding in early detection, highlighting the significant transformation in the healthcare industry due to advancements in disease detection, diagnosis, and treatment.

Preoperative 3-dimensional computed tomography bone density measures provide objective bone quality classifications for stemless anatomic total shoulder arthroplasty

Reproducible methods for determining adequate bone densities for stemless anatomic total shoulder arthroplasty (aTSA) are currently lacking. The purpose of this study was to evaluate the utility of preoperative computed tomography (CT) imaging for assessing the bone density of the proximal humerus for supportive differentiation in the decision making for stemless humeral component implantation. It was hypothesized that preoperative 3-dimensional (3-D) CT bone density measures provide objective classifications of the bone quality for stemless aTSA. A 3-part study was performed that included the analysis of cadaveric humerus CT scans followed by retrospective application to a clinical cohort and classification with a machine learning model. Thirty cadaveric humeri were evaluated with clinical CT and micro-CT (μCT) imaging. Phantom-calibrated CT data were used to extract 3-D regions of interest and defined radiographic scores. The final image processing script was applied retrospectively to a clinical cohort (n=150) that had a preoperative CT and intraoperative bone density assessment using the "thumb test," followed by placement of an anatomic stemmed or stemless humeral component. Postscan patient-specific calibration was used to improve the functionality and accuracy of the density analysis. A machine learning model (Support vector machine [SVM]) was utilized to improve the classification of bone densities for a stemless humeral component. The image processing of clinical CT images demonstrated good to excellent accuracy for cylindrical cancellous bone densities (metaphysis [ICC=0.986] and epiphysis [ICC=0.883]). Patient-specific internal calibration significantly reduced biases and unwanted variance compared with standard HU CT scans (P<.0001). The SVM showed optimized prediction accuracy compared with conventional statistics with an accuracy of 73.9% and an AUCof0.83 based on the intraoperative decision of the surgeon. The SVM model based on density clusters increased the accuracy of the bone quality classification to 87.3% with an AUC of 0.93. Preoperative CT imaging allows accurate evaluation of the bone densities in the proximal humerus. Three-dimensional regions of interest, rescaling using patient-specific calibration, and a machine learning model resulted in good to excellent prediction for objective bone quality classification. This approach may provide an objective tool extending preoperative selection criteria for stemless humeral component implantation.

Voxel-based dysconnectomic brain morphometry with computed tomography in Down syndrome.

Alzheimer's disease (AD) is a major health concern for aging adults with Down syndrome (DS), but conventional diagnostic techniques are less reliable in those with severe baseline disability. Likewise, acquisition of magnetic resonance imaging to evaluate cerebral atrophy is not straightforward, as prolonged scanning times are less tolerated in this population. Computed tomography (CT) scans can be obtained faster, but poor contrast resolution limits its function for morphometric analysis. We implemented an automated analysis of CT scans to characterize differences across dementia stages in a cross-sectional study of an adult DS cohort. CT scans of 98 individuals were analyzed using an automatic algorithm. Voxel-based correlations with clinical dementia stages and AD plasma biomarkers (phosphorylated tau-181 and neurofilament light chain) were identified, and their dysconnectomic patterns delineated. Dementia severity was negatively correlated with gray (GM) and white matter (WM) volumes in temporal lobe regions, including parahippocampal gyri. Dysconnectome analysis revealed an association between WM loss and temporal lobe GM volume reduction. AD biomarkers were negatively associated with GM volume in hippocampal and cingulate gyri. Our automated algorithm and novel dysconnectomic analysis of CT scans successfully described brain morphometric differences related to AD in adults with DS, providing a new avenue for neuroimaging analysis in populations for whom magnetic resonance imaging is difficult to obtain.

EXplainable artificial intelligence for automatic defect detection in additively manufactured parts using CT scan analysis

AbstractAdditive Manufacturing (AM) and in particular has gained significant attention due to its capability to produce complex geometries using various materials, resulting in cost and mass reduction per part. However, metal AM parts often contain internal defects inherent to the manufacturing process. Non-Destructive Testing (NDT), particularly Computed Tomography (CT), is commonly employed for defect analysis. Today adopted standard inspection techniques are costly and time-consuming, therefore an automatic approach is needed. This paper presents a novel eXplainable Artificial Intelligence (XAI) methodology for defect detection and characterization. To classify pixel data from CT images as pores or inclusions, the proposed method utilizes Support Vector Machine (SVM), a supervised machine learning algorithm, trained with an Area Under the Curve (AUC) of 0.94. Density-Based Spatial Clustering with the Application of Noise (DBSCAN) is subsequently applied to cluster the identified pixels into separate defects, and finally, a convex hull is employed to characterize the identified clusters based on their size and shape. The effectiveness of the methodology is evaluated on Ti6Al4V specimens, comparing the results obtained from manual inspection and the ML-based approach with the guidance of a domain expert. This work establishes a foundation for automated defect detection, highlighting the crucial role of XAI in ensuring trust in NDT, thereby offering new possibilities for the evaluation of AM components.

4D evolutions of cracks, voids, and corrosion products in reinforced concrete materials

This research paper presents a comprehensive investigation into the corrosion process in reinforced concrete structures using advanced analytical techniques, namely non-destructive X-ray computed tomography (CT) imaging, scanning electron microscopy (SEM) images, energy dispersive x-ray spectrometry (EDS), and Raman spectroscopy. The CT image analysis allowed for the identification and quantification of pore structures, crack propagation, and corrosion progression at different stages of corrosion. CT scanning and data analysis offer valuable 4D (3D spatial + time) insights into corrosion in reinforced concrete, revealing changes in pore sizes, with smaller pores increasing and larger pores decreasing as corrosion progresses. Our investigation reveals dynamic changes in reinforced concrete pores during the accelerated corrosion test leading to new pore formation and cracking. The research identifies two distinct types of cracks: one filled with corrosion products and the other, zipper-like cracks, resulting from the connection of deformed pores without corrosion products. The SEM images and EDS analysis confirmed the absence of corrosion products within these unique zipper cracks, suggesting a different mechanism of crack formation compared to the first type of cracks. The results revealed two distinct categories of corrosion products: iron oxides and iron hydroxides, with their distribution correlated to the duration of accelerated corrosion testing. The integration and verification of results from X-ray CT imaging and Raman spectroscopy established a comprehensive understanding of corrosion-induced damage in the reinforced concrete specimen, shedding light on complex interactions among different corrosion products during the corrosion process. These findings offer crucial insights for better understanding of the corrosion process in reinforced concrete paving the way for future development of effective treatments and strategies to mitigate corrosion impact.

Hematuria: Is it useful in predicting renal or ureteral stones in patient presenting to emergency department with flank pain?

The objective of the study was to evaluate hematuria as a diagnostic test for renal and ureteral stones compared with a noncontrast-enhanced computed tomography (CT) scan (gold standard test) in emergency room patients with acute flank pain. In total, 604 patients treated in our emergency department from 2006 to 2011, with a history of flank pain and suspected urolithiasis were included in a retrospective review. All patients were evaluated with a noncontrast-enhanced CT scan and urine analysis. Using the noncontrast CT scan as the gold standard for the evaluation of the presence, number, size, and site (renal or ureteral [upper, middle, and lower]) of the stones, we calculated the sensitivity, specificity, and positive and negative predictive values of hematuria for diagnosing both renal and ureteral stones. Urolithiasis was diagnosed in 388 patients (64%) and 216 patients (36%) had no stones on a noncontrast-enhanced CT scan. The sensitivity, specificity, positive predictive value, and negative predictive value for microhematuria were 77%, 33%, 67%, and 45%, respectively. Microhematuria was more common in patients with ureteral stones only (139 patients) and had a sensitivity of 85% compared to patients with renal stones only (32 patients), with a sensitivity of 55% (P < 0.001). There were no significant differences in the specificity or positive or negative predictive values. Although microhematuria is more sensitive to ureteral stones, the absence of microhematuria does not exclude the possibility of urolithiasis and a noncontrast-enhanced CT scan should be the gold standard diagnostic tool.

Preoperative assessment of grade, T stage, and lymph node involvement: machine learning-based CT texture analysis in colon cancer.

To investigate whether texture analysis of primary colonic mass in preoperative abdominal computed tomography (CT) scans of patients diagnosed with colon cancer could predict tumor grade, T stage, and lymph node involvement using machine learning (ML) algorithms. This retrospective study included 73 patients diagnosed with colon cancer. Texture features were extracted from contrast-enhanced CT images using LifeX software. First, feature reduction was performed by two radiologists through reproducibility analysis. Using the analysis of variance method, the parameters that best predicted lymph node involvement, grade, and T stage were determined. The predictive performance of these parameters was assessed using Orange software with the k-nearest neighbor (kNN), random forest, gradient boosting, and neural network models, and their area under the curve values were calculated. There was excellent reproducibility between the two radiologists in terms of 49 of the 58 texture parameters that were subsequently subject to further analysis. Considering all four ML algorithms, the mean AUC and accuracy ranges were 0.557-0.800 and 47-76%, respectively, for the prediction of lymph node involvement; 0.666-0.846 and 68-77%, respectively, for the prediction of grade; and 0.768-0.962 and 81-88%, respectively, for the prediction of T stage. The best performance was achieved with the random forest model in the prediction of LN involvement, the kNN model for the prediction of grade, and the gradient boosting model for the prediction of T stage. The results of this study suggest that the texture analysis of preoperative CT scans obtained for staging purposes in colon cancer can predict the presence of advanced-stage tumors, high tumor grade, and lymph node involvement with moderate specificity and sensitivity rates when evaluated using ML models.

Premorbid glenoid anatomy reconstruction from contralateral shoulder 3-dimensional measurements: a computed tomography scan analysis of 260 shoulders

Total shoulder arthroplasty (TSA) aims to reconstruct the premorbid anatomy of a pathologic shoulder. A healthy contralateral shoulder could be useful as a template in planning TSA. The symmetry between the left and right shoulders in healthy patients remains to be proved. The purpose of this study was to compare the 3-dimensional anatomy of the glenoid between sides in a healthy population. A multinational computed tomography scan database was retrospectively reviewed for all healthy bilateral shoulders in patients aged between 18 and 50 years. One hundred thirty pairs of healthy shoulder computed tomography scans were analyzed, and glenoid version, inclination, width, and height, as well as glenoid lateral offset and scapula lateral offset, were measured. All anatomic measures were computed with Blueprint, validated 3-dimensional planning software. The intraclass correlation coefficient was determined for each measure between left and right shoulders. The minimal detectable change (MDC) was calculated using the following formula: MDC=2×1.96×Standarderrorofmeasurement. The comparison between 130 pairs of healthy scapulae showed statistically significant differences in absolute values between right and left glenoid version (-5.3° vs. -4.6°, P<.01), inclination (8.4° vs. 9.3°, P<.01), and width (25.6 mm vs. 25.4 mm, P<.01), as well as scapula offset (105.8 mm vs. 106.2 mm, P<.01). Glenoid height was comparable between right and left shoulders (33.3 mm vs. 33.3 mm, P=.9). The differences between the means were always inferior to the MDC regarding glenoid version, inclination, height, and width, as well as scapula offset. Very strong intraclass correlation coefficients between the left and right shoulders were found for all evaluated paired measures. Healthy contralateral scapulae are highly reliable to predict inclination, height, width, and scapula offset and are reliable to predict version of a given scapula. Paired right and left scapulae were not statistically symmetrical regarding mean glenoid version, inclination, and width, as well as scapula offset. Nevertheless, the reported differences were not higher than the MDC for this cohort, confirming that healthy contralateral shoulders can be a useful template in TSA preoperative planning.

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images

Lung cancer (LC) remains a leading cause of death worldwide. Early diagnosis is critical to protect innocent human lives. Computed tomography (CT) scans are one of the primary imaging modalities for lung cancer diagnosis. However, manual CT scan analysis is time-consuming and prone to errors/not accurate. Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. In the present article, we proposed a novel transfer learning-based predictor called, Lung-EffNet for lung cancer classification. Lung-EffNet is built based on the architecture of EfficientNet and further modified by adding top layers in the classification head of the model. Lung-EffNet is evaluated by utilizing five variants of EfficientNet i.e., B0–B4. The experiments are conducted on the benchmark dataset “IQ-OTH/NCCD” for lung cancer patients grouped as benign, malignant, or normal based on the presence or absence of lung cancer. The class imbalance issue was handled through multiple data augmentation methods to overcome the biases. The developed model Lung-EffNet attained 99.10% of accuracy and a score of 0.97 to 0.99 of ROC on the test set. We compared the efficacy of the proposed fine-tuned pre-trained EfficientNet with other pre-trained CNN architectures. The predicted outcomes demonstrate that EfficientNetB1 based Lung-EffNet outperforms other CNNs in terms of both accuracy and efficiency. Moreover, it is faster and requires fewer parameters to train than other CNN based models, making it a good choice for large-scale deployment in clinical settings and a promising tool for automated lung cancer diagnosis from CT scan images.

Adiposity and Coagulation: Predicting Postinjury Coagulation With Advanced Imaging Analysis

IntroductionAnatomic distribution of adipose tissue has demonstrated variable associations with hypercoagulability. Utilizing a retrospective analysis of a previously enrolled prospective cohort, we assessed computed tomography (CT) scan-based anthropometric and volumetric measures of adiposity as predictors of postinjury hypercoagulability. MethodsSegmentation analysis of arrival CT scans in significantly injured patients at a single level-I trauma center enrolled from December 2017 to August 2021 were analyzed for anthropometric indices of waist circumference (WC) and sagittal abdominal diameter (SAD), and volumetric parameters of visceral adipose tissue, superficial/deep subcutaneous adipose tissue, psoas/paravertebral muscle volume, and abdominal wall muscle volume. Associations with thromboelastography (TEG) were explored. ResultsData from 91 patients showed strong correlations between body mass index and standard anthropometric measures of WC and SAD (P < 0.001); calculated volumes of subcutaneous adipose tissue and visceral adipose tissue (P < 0.001); and ratios of subcutaneous adipose:psoas muscle (SP ratio) and visceral adipose:psoas muscle ratio (both with P < 0.001, respectively). Correlation between TEG maximal amplitude (MA) and body mass index and SAD were not significant, with only weak correlation between TEG-MA and WC (r = 0.238, P = 0.041). Moderate but significant correlations existed between SP ratio and TEG-MA (r = 0.340, P = 0.005), but not visceral adipose:psoas muscle ratio (r = 0.159, P = 0.198). The relationship between TEG-MA and SP ratio remained significant when adjusted for injury severity score and lactate level (b = 0.302, P = 0.001). ConclusionsSP ratio is more strongly correlated with TEG-MA than standard obesity measures, and independently predicts increasing clot strength/stability after injury. Coagulation-relevant measures of sarcopenic obesity can be measured on CT scan, and may be used to optimize thromboprophylaxis strategies for obese injured patients.

Computed Tomography Scan Evidence for Left Atrial Appendage Short-Term Remodeling Following Percutaneous Occlusion: Impact of Device Oversizing.

Background The interrelationships between left atrial appendage (LAA) dimensions and device following implantation are unknown. We aimed to analyze the impact of Watchman device implantation on LAA dimensions following its percutaneous closure and potential predictors of remodeling. Methods and Results All consecutive LAA closure procedures performed at 2 centers between November 2017 and December 2020 were included in the WATCH-DUAL (Watchman 2.5 Versus Watchman FLX in a Dual-Center Left Atrial Appendage Closure Cohort) registry. This study included patients who had pre- and postintervention computed tomography scan analysis. The LAA and device dimensions were measured in a centralized core lab by 3-dimensional computed tomography scan reconstruction methods, focusing on the device landing zone. This analysis included 104 patients (age, 76.0 [range, 72.0-83.0] years; 72% men; 53% Watchman FLX; 47% Watchman 2.5). The baseline characteristics were comparable between Watchman 2.5 and Watchman FLX groups, except for the higher use of oversizing in the latter group. The median delay for computed tomography control was 49 (range, 43-64) days. The landing zone area (median, 446 [range, 363-523] versus 290 [222-366] mm2; P<0.001) and minimal diameter (median, 23.0 [range, 20.7-24.8] versus 16.7 [14.7-19.4] mm; P<0.001) significantly increased after implantation. The absolute (median, 157 [range, 98-220] versus 85 [18-148] mm2, P<0.001) and relative (median, 50% [range, 32%-79%] versus 26% [4%-50%]; P<0.001) increases in landing zone area were more pronounced in patients with oversized device. Baseline LAA dimensions were smaller, landing zone eccentricity larger, and oversized device more frequent in patients with significant overexpansion compared with the others. Conclusions LAA dimensions increased at the site of the Watchman prosthesis after implantation, suggesting a local positive remodeling after the procedure. This phenomenon was more pronounced in the case of oversized devices.

Advances in neonatal brain imaging: A comparative analysis of MRI, CT scans, and ultrasound

This scholarly investigation undertakes a comprehensive comparison of the diagnostic efficacy, precision, and sensitivity associated with neonatal brain Magnetic Resonance Imaging (MRI) in contrast to its counterparts, Computed Tomography (CT) scans and ultrasound. As the medical community has progressively become attuned to the long-term health implications of radiation exposure from CT scans, the imperative of a strategy mitigating this risk has gained prominence. In this context, ultrasound emerges as an alternative modality devoid of ionizing radiation. Employing a methodical approach rooted in systematic literature review, this study synthesizes five pertinent research works to unravel its research objectives. Empirical evidence substantiates that neonatal brain MRI surpasses both neonatal brain CT and ultrasound in diagnostic effectiveness. The underpinning rationale for this phenomenon lies in the heightened accuracy inherent to neonatal brain MRI procedures. To unravel the intricacies associated with disparities between neonatal and adult brain MRI procedures, the study meticulously investigates structural, shape, and size distinctions. This endeavor underscores the necessity for bespoke MRI apparatuses designed to account for these nuances. In pursuit of this objective, the integration of technologically advanced components such as compact scanners and refinements in magnetic and coil technologies has engendered tangible improvements. This innovation confluence bears testimony to the augmentation of patient safety, conferring a cascade effect wherein the precision of acquired MRI data underwrites accurate diagnoses and consequent therapeutic interventions. Conclusively, the study underscores the pivotal role played by recent MRI technological advancements in amplifying its efficacy within the niche domain of neonatal brain imaging. Prospective innovations within the MRI ambit stand poised to recalibrate performance benchmarks, thereby amplifying its diagnostic potency and broadening its scope of application.

What is the prevalence and distribution of narrow dysplastic and fully corticalized pedicles in Asian adolescent idiopathic scoliosis patients with major main thoracic curves? A computed tomography scan analysis of 6,494 pedicles

BACKGROUND CONTEXTPatients with adolescent idiopathic scoliosis (AIS) have higher prevalence of abnormal or dysplastic pedicles. PURPOSETo investigate the prevalence and distribution of narrow dysplastic and fully corticalized pedicles in Asian AIS patients with major main thoracic curves. DESIGNRetrospective study. PATIENT SAMPLEA total of 6,494 pedicles in 191 patients were measured and evaluated. OUTCOME MEASURESThe primary outcomes measures were the pedicle width measurements (total transverse pedicle width, transverse cancellous width, total transverse cortical width) and classification of pedicles. Demographic data (age, gender, height, weight, body mass index), proximal thoracic Cobb angle, main thoracic Cobb angle and lumbar Cobb angle were also obtained. METHODSAIS patients with major (largest Cobb angle) main thoracic curves and had computed tomography (CT) scans prior to corrective spine surgery were reviewed. The pedicles were classified as Grade A: cancellous channel >4 mm; Grade B: cancellous channel 2 to 4 mm; Grade C: cancellous channel <2 mm or corticalized pedicle >4 mm; Grade D: corticalized pedicle ≤4 mm. Grades B, C, and D were dysplastic pedicles while grades C and D were narrow dysplastic pedicles. RESULTSThe prevalence of dysplastic pedicles (grades B, C, and D) was 61.7%. There were 22.6% narrow dysplastic pedicles (grades C and D) and 4.1% fully corticalized pedicles (grade D). In the thoracolumbar region, there was a sharp transition from larger and less dysplastic pedicles at T11 and T12 to narrower and more dysplastic pedicles at L1 and L2 (narrow dysplastic pedicles at T11: 3.1%, T12: 3.1%, L1: 39.8% and L2: 23.6%). Higher prevalences of narrow dysplastic pedicles were located at right T3 to T5 (71.2%–83.7%) and left T7-T9 (51.3%–61.2%). Higher prevalences of fully corticalized pedicles were located at right T3 to T5 (20.9%–34.0%) and left T7 to T8 (11.0%–12.0%). These were the concave pedicles of proximal thoracic and main thoracic curves, respectively. CONCLUSIONThere were 95.9% pedicles with cancellous channels (grades A, B, and C) can allow pedicle screw fixation and only 4.1% fully corticalized pedicles (grade D) that require an alternative method of fixation. For grade C pedicles (18.5%), pedicle screws can still be attempted with caution. Precautions should also be observed at the L1 and L2 levels as there was a transition to narrower pedicles.

Artificial intelligence in the diagnosis of thoracic aortic aneurysms in a retrospective chest computed tomography scan analysis

BACKGROUND: Aortic aneurysms, known as silent killers, are frequently asymptomatic, leading to vessel rupture and death. Annual rates for ruptures, stratifications, and sudden deaths are up to 3.6%, 3.7%, and 10.8%, respectively. Timely diagnosis and early treatment save a patients life. The use of artificial intelligence (AI) technologies helps to detect aortic aneurysms, which significantly improves the quality of diagnosis and saves patients lives.&#x0D; AIM: To assess the efficiency of using AI technologies to detect thoracic aortic aneurysms on chest computed tomography (CT) and determining the possibility of using AI technologies as an assistant to the radiologist during the primary description of radiological images.&#x0D; METHODS: The study retrospectively assessed the results of AI technologies aimed at detecting thoracic aortic aneurysms on chest CT scans. No contrast enhancement was performed primarily. The sample consisted of 84,405 observations of patients over the age of 18 years; of these, 86 scans with thoracic aortic aneurysms were selected according to AI data. The selected examinations were retrospectively assessed by radiologists and vascular surgeons for the probable presence of a thoracic aortic aneurysm. In 44 cases, an aortic aneurysm was initially detected by the radiologist. In 31 cases, an aneurysm was not initially described by the radiologist, 6 were excluded from the sample (due to the absence of the radiologists report in the Unified Radiological Information Service), and 5 scans had false-positive results according to AI findings.&#x0D; RESULTS: The use of AI technologies allows detection and labeling of pathological changes in the aorta on the images. AI technologies increase the detectability of thoracic aortic aneurysms in the description of chest CT scans by 38.8%. The incidence of ascending aortic aneurysm was 0.3%, which corresponded to the literature data of 0.16%1.6%. According to the results, 22 surgical interventions for aortic stenting were performed.&#x0D; CONCLUSIONS: The use of AI in the primary chest CT description may help increase the detectability of clinically significant pathological conditions, such as thoracic aortic aneurysm. Further development of routing for this category of patients in the cito mode for surgical treatment is relevant. Expansion of retrospective screening by chest CT scans using AI systems will improve the quality of diagnosis of concomitant pathologies and prevent adverse outcomes for patients.