Current Medical Imaging - Online First

Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy.

This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abnormalities or diseases from endoscopic images via the fusion of residual blocks, including alpha dropouts (αDO) and auxiliary fusing layers.

To automatically diagnose colon disorders from medical images, this work explores the use of a fused deeplearning model that incorporates the EfficientNetB0, MobileNetV2, and ResNet50V2 architectures. By integrating these features, the fused model aims to improve the classification accuracy and robustness for various colon diseases. The proposed model incorporates an auxiliary fusion layer and a fusion residual block. By combining diverse features through an auxiliary fusion layer, the network can create more comprehensive and richer representations, capturing intricate patterns that might be missed by single-source processing. The fusion residual block incorporates residual connections, which help mitigate the vanishing gradient problem. By adding the input of the block directly to its output, these connections facilitate better gradient flow during backpropagation, allowing for deeper and more stable training. A wide range of endoscopic images are used to assess the proposed model, offering an accurate depiction of various disease scenarios.

The proposed model, with an auxiliary fusion layer and residual blocks, exhibited an enormous reduction in overfitting and performance saturation. The proposed model achieved an impressive 98.03% training accuracy and 97.90% validation accuracy after evaluation, outperforming the majority of typically trained DCNNs in terms of efficiency and accuracy.

The proposed method developed a lightweight model that correctly identifies disorders of the gastrointestinal (GI) tract by combining advanced techniques, including feature fusion, residual learning, and self-normalization.

Laparoscopically Harvested Pedicled Omental Flap [LHPOF] has become a viable option for breast reconstruction due to advancements in minimally invasive techniques, offering benefits like reduced postoperative pain and minimal scarring.

This study examines the imaging findings in eight patients who underwent breast reconstruction using a LHPOF. Imaging modalities, including mammography, ultrasonography, MRI, and CT, consistently showed reconstructed breasts with fat replacing glandular tissue and numerous internal vessels. One case of recurrence was detected, demonstrating the efficacy of conventional surveillance imaging studies in facilitating the detection of recurrences.

This is the first report detailing imaging findings of breast reconstruction using an LHPOF, including a recurrence case. Understanding these imaging results is crucial for effective surveillance in breast cancer patients with omental flap reconstruction.

In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.

Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.

These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViT-B-16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.

This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

This study aimed to verify whether the Liver-to-portal Ratio (LPR) can assess the adequacy of the Hepatobiliary Phase (HBP) for patients with different liver functions.

A total of 125 patients were prospectively enrolled in the study and graded into the non-cirrhosis group (45), Child-Pugh A group (40), and Child-Pugh B/C group (40). The LPR on HBP was calculated after eight HBPs were obtained within 5-40 minutes. The adequate HBP was determined according to the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus statement. The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were analyzed by paired t-test and Wilcoxon signed-rank test, respectively. The chi-square test was used to test the difference in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP.

The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were significant in Child-Pugh A and Child-Pugh B/C groups (P < 0.05), except for the non-cirrhosis group (P > 0.05). The differences in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP were not statistically significant in all groups (all P > 0.05).

The adequate HBP obtained according to the 2016 ESGAR consensus statement could provide larger LPR and better lesions’ conspicuity than 10-min HBP, especially for cirrhotic patients; however, the efficacy of using an LPR cutoff of 1.462 as the standard of the adequate HBP may be compromised in patients with cirrhosis.