Anesthesia, General ; Computer Systems ; Equipment Design ; methods ; Equipment Failure ; Humans ; Male ; Middle Aged ; Monitoring, Intraoperative ; instrumentation ; methods ; Monitoring, Physiologic ; methods ; User-Computer Interface

Anesthesiology ; methods ; Catheterization ; Equipment Failure ; Humans ; Infusions, Intravenous ; instrumentation ; Injections, Spinal ; instrumentation ; Needles ; Plastics

Objectives: Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability. Methods: In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models—namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny—followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks—namely Resnet50, EfficientnetB3, and ConvNeXtTiny—that were classified using the XGBoost classifier. Results: The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples. Conclusion BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.

Objectives: Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability. Methods: In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models—namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny—followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks—namely Resnet50, EfficientnetB3, and ConvNeXtTiny—that were classified using the XGBoost classifier. Results: The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples. Conclusion BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.

Objectives: Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability. Methods: In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models—namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny—followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks—namely Resnet50, EfficientnetB3, and ConvNeXtTiny—that were classified using the XGBoost classifier. Results: The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples. Conclusion BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.

Objectives: Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability. Methods: In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models—namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny—followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks—namely Resnet50, EfficientnetB3, and ConvNeXtTiny—that were classified using the XGBoost classifier. Results: The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples. Conclusion BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.

Objectives: Breast cancer poses a significant global health challenge, characterized by complex origins and the potential for life-threatening metastasis. The critical need for early and accurate detection is underscored by the 685,000 lives claimed by the disease worldwide in 2020. Deep learning has made strides in advancing the prompt diagnosis of breast cancer. However, obstacles persist, such as dealing with high-dimensional data and the risk of overfitting, necessitating fresh approaches to improve accuracy and real-world applicability. Methods: In response to these challenges, we propose BCED-Net, which stands for Breast Cancer Ensemble Diagnosis Network. This innovative framework leverages transfer learning and the extreme gradient boosting (XGBoost) classifier on the Breast Cancer RSNA dataset. Our methodology involved feature extraction using pre-trained models—namely, Resnet50, EfficientnetB3, VGG19, Densenet121, and ConvNeXtTiny—followed by the concatenation of the extracted features. Our most promising configuration combined features extracted from deep convolutional neural networks—namely Resnet50, EfficientnetB3, and ConvNeXtTiny—that were classified using the XGBoost classifier. Results: The ensemble approach demonstrated strong overall performance with an accuracy of 0.89. The precision, recall, and F1-score values, which were all at 0.86, highlight a balanced trade-off between correctly identified positive instances and the ability to capture all actual positive samples. Conclusion BCED-Net represents a significant leap forward in addressing persistent issues such as the high dimensionality of features and the risk of overfitting.

The presence of anterior hip dislocation along with contralateral posterior hip dislocation in the absence of other major traumas is a distinctly rare injury pattern. We report such a case, along with a review of previous cases. A 40-year-old male patient after motorcycle skidding had posterior dislocation of the left hip and anterior dislocation of the right one without other associated injuries. The patient underwent successful closed reduction of both hips. The clinical course and follow-up assessment of the patient was uneventful.
Hip ; Hip Dislocation ; Humans ; Multiple Trauma

Simultaneous isolated bilateral patellar fractures are very rare injuries and most often associated with systemic disorders such as hyperparathyroidism, osteoporosis, stress fracture and kidney failure. Isolated bilateral traumatic fracture of patella following an unusual mode of injury is seldomly reported in the literature. We reported such a case following a road traffic accident without any associated injuries or co-morbid condition. The patella on the right side had transverse open fracture which was fixed with two Kirschner wires following tension band principle, and that on the left side sustained upper pole comminution which was treated by partial patellectomy. The patient achieved good outcome: at 6 months he was able to squat and sit cross legged; at one year he obtained nearly normal muscle strength and full range of motion. We discussed the injury mechanism, management and rehabilitation in such a case and reviewed the available literature regarding such a presentation.
Bone Wires ; Fracture Fixation, Internal ; Fractures, Bone ; Fractures, Comminuted ; Humans ; Knee Injuries ; Patella ; injuries

The presence of anterior hip dislocation along with contralateral posterior hip dislocation in the absence of other major traumas is a distinctly rare injury pattern.We report such a case,along with a review of previous cases.A 40-year-old male patient after motorcycle skidding had posterior dislocation of the left hip and anterior dislocation of the right one without other associated injuries.The patient underwent successful closed reduction of both hips.The clinical course and follow-up assessment of the patient was uneventful.