Experimental evidence supporting the existence of the viscerosomatic reflex highlights an involvement of multiple vertebral levels when renal pathology is present. Further exploration of this reflex, particularly in the context of nephrolithiasis, could offer valuable insights for osteopathic treatments related to this pathology. Open-sourced machine learning datasets provide a valuable source of imaging data for investigating osteopathic phenomena including the viscerosomatic reflex. This study aimed to compare the rotation of vertebrae at levels associated with the viscerosomatic reflex in renal pathology in patients with nephrolithiasis vs. those without kidney stones. A total of 210 unenhanced computed tomography (CT) scans were examined from an open-sourced dataset designed for kidney and kidney stone segmentation. Among these, 166 scans were excluded due to pathologies that could affect analysis (osteophytes, renal masses, etc.). The 44 scans included in the analysis encompassed 292 relevant vertebrae. Of those, 15 scans were of patients with kidney stones in the right kidney, 13 in the left kidney, 7 bilaterally, and 11 without kidney stones. These scans included vertebral levels from T5-L5, with the majority falling within T10-L5. An open-sourced algorithm was employed to segment individual vertebrae, generating models that maintained their orientation in three-dimensional (3D) space. A self-coded 3D slicer module utilizing vertebral symmetry for rotation detection was then applied. Two-way analysis of variance (ANOVA) testing was conducted to assess differences in vertebral rotation between the four possible combinations of kidney stone location (left-sided, right-sided, bilateral, or none) and vertebral levels (T10-L4). Subsequently, the two-way ANOVA analysis was narrowed down to include various combinations of three vertebral levels (T10-L4) to identify the most significant levels. We observed a statistically significant difference in average vertebral rotation (p=0.0038) dependent on kidney stone location. Post-hoc analysis showed an average difference in rotation of -1.38° leftward between scans that contained left kidney stones compared to no kidney stones (p=0.027), as well as an average difference of -1.72° leftward in the scans containing right kidney stones compared to no kidney stone (p=0.0037). The average differences in rotation between the remaining stone location combinations were not statistically significant. Narrowed analysis of three vertebral level combinations showed a single statistically significant combination (T10, T12, and L4) out of a total of 35 combinations (p=0.028). A subsequent post-hoc procedure showed that angular rotation at these levels had the only statistically significant contribution to the difference between scans containing right kidney stones and no kidney stones (p=0.046). This study observed a statistically significant difference in the rotation of vertebrae at the levels associated with the viscerosomatic reflex between patients with unilateral kidney stones and those without kidney stones. The vertebral levels with the highest significance of association with this finding, particularly in right kidney stones, were T10, T12, and L4.

To investigate the potential feasibility of ultra-low-dose (ULD) liver CT with the artificial intelligence iterative reconstruction (AIIR). Sixty-five patients who underwent triphasic contrast-enhanced liver CT were prospectively enrolled. Low tube voltage (80/100 kV) and tube current (35 to 78 mAs) were set in both portal venous phase (PVP) and delayed phase (DP). For each phase, an ULD acquisition (1.11 to 2.50 mGy) was taken followed immediately by a routine-dose (RD) acquisition (11.71 to 19.73 mGy). RD images were reconstructed with a hybrid iterative reconstruction algorithm (RD-HIR), while ULD images were reconstructed with both HIR (ULD-HIR) and AIIR (ULD-AIIR). The noise power spectrum (NPS) noise magnitude, average NPS spatial frequency, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for the quantitative assessment. Qualitative assessment was performed by 2 radiologists who independently scored the images for diagnostic acceptance. In addition, the radiologists identified focal lesions and characterized noncystic lesions as benign or malignant with both RD and ULD liver CT. Among the enrolled patients (mean age: 58.6±12.9 y, 35 men), 234 lesions with a mean size of 1.27±1.56 cm were identified. In both phases, ULD-AIIR showed comparable NPS noise magnitude with RD-HIR (all P>0.017), and lower NPS noise than ULD-HIR (all P<0.001). Average NPS spatial frequency, SNR, and CNR were highest with ULD-AIIR, followed by RD-HIR and ULD-HIR (all P<0.001). ULD-AIIR showed comparable diagnostic acceptance scores with RD-HIR, while ULD-HIR failed to meet the diagnostic acceptance requirements. RD-HIR and ULD-AIIR achieved comparable detection rate (99.6% vs. 99.1%) and area under curve (AUC) of the receiver operating characteristic curve (ROC) in classifying benign (n=46) and malignant (n=58) noncystic lesions (0.98 vs. 0.97, P=0.3). With AIIR, it is potentially feasible to achieve ULD liver CT (60% dose reduction) while preserving the image and diagnostic quality.

Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable automated medical image analysis for detecting and delineating tumors in CT images, thereby reducing clinicians' workload. Achieving generalization capabilities in limited data domains, such as radiology, requires modern DL models to be trained with image augmentation. However, naively applying augmentation methods developed for natural images to CT scans often disregards the nature of the CT modality, where the intensities measure Hounsfield Units (HU) and have important physical meaning. This paper challenges the use of such intensity augmentations for CT imaging and shows that they may lead to artifacts and poor generalization. To mitigate this, we propose a CT-specific augmentation technique, called Random windowing, that exploits the available HU distribution of intensities in CT images. Random windowing encourages robustness to contrast-enhancement and significantly increases model performance on challenging images with poor contrast or timing. We perform ablations and analysis of our method on multiple datasets, and compare to, and outperform, state-of-the-art alternatives, while focusing on the challenge of liver tumor segmentation.

Non-ST-elevation acute coronary syndrome (NSTE-ACS) remains a diagnostic challenge, as a proportion of patients do not present with obstructive coronary lesions. Coronary computed tomography angiography (CCTA) has emerged as a non-invasive tool for coronary assessment, and integrating artificial intelligence (AI) may enhance its diagnostic accuracy. This study evaluates a machine learning (ML) model using a learned fusion approach to identify culprit lesions in high-risk NSTE-ACS patients. This study is a sub-analysis of a prospective, multicenter trial including patients with high-risk NSTE-ACS who underwent CCTA, followed by ICA and fractional flow reserve (FFR) assessment in every intermediate stenosis. An ML framework was developed to analyze 2 orthogonal CCTA views of each coronary segment and classify them as culprit or non-culprit, with ICA +/- FFR as gold standards. The model was trained using 5-fold cross-validation and compared against 5 baseline methods, including conventional feature extraction and FFR-CT. Among 80 patients, 514 coronary segments were analyzed, with 63 (12.3%) labeled as culprit. The learned fusion model achieved a sensitivity of 0.55 ± 0.14, specificity of 0.93 ± 0.05, and F1-score of 0.53 ± 0.11. The AUC was 0.84 ± 0.06, matching the performance of FFR-CT (AUC of 0.82 ± 0.08). Our findings demonstrate that the learned fusion approach, based on combining two orthogonal views, achieved a performance level comparable to that of FFR-CT, as shown by the AUC of both techniques. These results confirm that AI-driven CCTA analysis could enhance clinical decision-making in high-risk NSTE-ACS patients, warranting further validation of this method in larger cohorts.

This study aimed to develop and validate a machine learning-based computed tomography (CT) radiomics method to preoperatively predict the presence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC). A total of 921 patients with histopathologically proven PTMC from three medical centers were included in this retrospective study and divided into training, internal validation, external test 1, and external test 2 sets. Radiomics features of thyroid tumors were extracted from CT images and selected for dimensional reduction. Five machine learning classifiers were applied, and the best classifier was selected to calculate radiomics scores (rad-scores). Then, the rad-scores and clinical factors were combined to construct a nomogram model. In the four sets, 35.18% (324/921) patients were CLNM+. The XGBoost classifier showed the best performance, with the highest average area under the curve (AUC) of 0.756 in the validation set. The nomogram model incorporating XGBoost-based rad-scores with age and sex showed better performance than the clinical model in the training [AUC: 0.847(0.809-0.879) vs. 0.706(0.660-0.748)], internal validation [AUC: 0.773(0.682-0.847) vs. 0.671(0.575-0.758)], external test 1 [AUC: 0.807(0.757-0.852) vs. 0.639(0.580-0.695)], and external test 2 [AUC: 0.746(0.645-0.830) vs. 0.608(0.502-0.707)] sets. Furthermore, the nomogram showed better clinical benefit than the clinical and radiomics models. The nomogram model based on the XGBoost classifier exhibited favorable performance. This model provides a potential approach for the non-invasive diagnosis of CLNM in patients with PTMC. This study developed a potential surrogate of preoperative accurate evaluation of CLNM status, which is non-invasive and easy-to-use.

Separate renal function assessment is important in clinical decision making. The single-photon emission computed tomography is commonly used for the assessment although radioactive, tedious and of high cost. This study aimed to automatically assess the separate renal function using plain CT images and artificial intelligence methods, including deep learning-based automatic segmentation and radiomics modeling. We performed a retrospective study on 281 patients with nephrarctia or hydronephrosis from two centers (Training set: 159 patients from Center I; Test set: 122 patients from Center II). The renal parenchyma and hydronephrosis regions in plain CT images were automatically segmented using deep learning-based U-Net transformers (UNETR). Radiomic features were extracted from the two regions and used to build radiomic signature using the ElasticNet, then further combined with clinical characteristics using multivariable logistic regression to obtain an integrated model. The automatic segmentation was evaluated using the dice similarity coefficient (DSC). The mean DSC of automatic kidney segmentation based on UNETR was 0.894 and 0.881 in the training and test sets. The average time of automatic and manual segmentation was 3.4 s/case and 1477.9 s/case. The AUC of radiomic signature was 0.778 in the training set and 0.801 in the test set. The AUC of the integrated model was 0.792 and 0.825 in the training and test sets. It is feasible to assess the renal function of each kidney separately using plain CT and AI methods. Our method can minimize the radiation risk, improve the diagnostic efficiency and reduce the costs.

Cervical spine fractures present considerable challenges in both diagnosis and treatment. With the increasing incidence of such injuries and the limitations of conventional diagnostic tools, there is a pressing demand for more precise and effective detection methods. This study proposes a robust Multi-class Classification model for Cervical Spine Fractures (MC-CSF) using Computed Tomography (CT) images to enable the precise identification of fracture types. The process of MC-CSF starts with preprocessing input images using an Enhanced Wiener Filtering (EWF) technique to minimize noise while retaining critical structural features. Following this, a Modified Residual Block-assisted ResUNet (MRB-RUNet) model is utilized for segmentation to precisely isolate the cervical spine area. Once segmented, feature extraction combines both deep learning approaches and texture-based analysis, in which deep features are extracted from established models like VGG16 and Residual Network (ResNet), while Local Gabor Transitional Pattern (LGTrP) captures subtle local texture variations. These features are then processed by an ensemble of sophisticated classifiers, including Enhanced LeNet (E-LNet), ShuffleNet, and a deep convolutional neural network (DCNN), each tasked with distinguishing between different fracture types. To enhance overall classification accuracy, a soft voting approach is applied, where the probabilistic outputs of multiple classifiers are aggregated. This strategy leverages the complementary strengths of individual models, resulting in a more robust and reliable prediction of cervical spine fracture categories. The Ensemble model consistently outperforms the traditional approaches with peak accuracy of 0.954, precision of 0.813 and NPV of 0.974, respectively.

Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the perspective of biomechanics, rupture occurs when the stress acting on the aortic wall exceeds the wall strength. Wall stress distribution can be obtained by computational biomechanical analyses, especially structural Finite Element Analysis. For risk assessment, probabilistic rupture risk of TAA can be calculated by comparing stress with material strength using a material failure model. Although these engineering tools are currently available for TAA rupture risk assessment on patient specific level, clinical adoption has been limited due to two major barriers: labor intensive 3D reconstruction current patient specific anatomical modeling still relies on manual segmentation, making it time consuming and difficult to scale to a large patient population, and computational burden traditional FEA simulations are resource intensive and incompatible with time sensitive clinical workflows. The second barrier was successfully overcome by our team through the development of the PyTorch FEA library and the FEA DNN integration framework. By incorporating the FEA functionalities within PyTorch FEA and applying the principle of static determinacy, we reduced the FEA based stress computation time to approximately three minutes per case. Moreover, by integrating DNN and FEA through the PyTorch FEA library, our approach further decreases the computation time to only a few seconds per case. This work focuses on overcoming the first barrier through the development of an end to end deep neural network capable of generating patient specific finite element meshes of the aorta directly from 3D CT images.

This study aimed to evaluate the diagnostic performance of machine learning (ML) algorithms based on radiomic features extracted from cone-beam computed tomography (CBCT) images in differentiating the nasopalatine duct (NPD) from the nasopalatine duct cyst (NPDC), and to compare their performance with that of a dentomaxillofacial radiologist. CBCT scans from 101 histopathologically confirmed NPDC cases and 101 age- and sex-matched controls with normal NPD were retrospectively analyzed. Manual segmentation was performed to extract 1037 radiomic features (original, Laplacian of Gaussian, and wavelet-transformed). After dimensionality reduction, five ML models (support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), and logistic regression (LR)) were trained using 5-fold cross-validation. Performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, precision, recall, and F1-score. Among the 11 optimal features identified through feature selection, large area high gray level emphasis and zone variance from the gray level size zone matrix (GLSZM) class were the most prominent. SVM achieved the highest performance in the test set (AUC and all other metrics = 1.00). The radiologist showed comparable but slightly lower overall performance than SVM (AUC = 0.94, with other metrics between 0.93 and 0.95). Machine learning algorithms based on radiomic features extracted from CBCT images can effectively differentiate NPD from NPDC. Unlike standard visual interpretation, this approach analyzes quantitative image features via mathematical models, yielding objective and reproducible results. It may serve as a non-invasive, complementary decision-support tool, particularly in diagnostically challenging cases.

Computed Tomography (CT)has gained recognition as the leading imaging method, extensively used in the diagnosis of spinal cord injuries. The reliance on CT imaging for acute care in patients with Spinal Cord Injury (SCI) has expanded rapidly. However, the diagnosis of initial clinical injury is crucial to accurately predict functional prediction, which is a difficult task for both clinicians and radiologists. To conquer this issue, an efficient model based on SCI detection is proposed, named as Shepard Parallel Convolutional Forward Harmonic Net (ShPCFHNet). The first step involves improving the CT image by applying logarithmic transformations in the enhancement phase. Spinal cord segmentation is then performed with the aid of the proposed Dual-branch UNet, whose loss function is adapted using Sensitivity-Specificity Loss (SSL). Following this, disc localization is carried out using an active contour model, and feature extraction is subsequently performed. The final step involves detecting SCI using ShPCFHNet, which combines the Shepard Convolutional Neural Network (ShCNN) and Parallel Convolutional Neural Network (PCNN) with Harmonic analysis. The proposed model achieved performance metrics of 91.397% accuracy, 92.684% True Positive Rate (TPR), and 90.366% True Negative Rate (TNR).