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.

Background: Existing literature lacks high-quality evidence regarding the ideal intraoperative positive end-expiratory pressure (PEEP) to minimize postoperative pulmonary complications (PPCs). We hypothesized that applying individualized PEEP derived from electrical impedance tomography would reduce the severity of postoperative lung aeration loss, deterioration in oxygenation, and PPC incidence. Methods: A pilot feasibility study was conducted on 36 patients who underwent open abdominal oncologic surgery. The patients were randomized to receive individualized PEEP or conventional PEEP at 4 cmH2O. The primary outcome was the impact of individualized PEEP on changes in the modified lung ultrasound score (MLUS) derived from preoperative and postoperative lung ultrasonography. A higher MLUS indicated greater lung aeration loss. The secondary outcomes were the PaO2/FiO2 ratio and PPC incidence. Results: A significant increase in the postoperative MLUS (12.0 ± 3.6 vs 7.9 ± 2.1, P < 0.001) and a significant difference between the postoperative and preoperative MLUS values (7.0 ± 3.3 vs 3.0 ± 1.6, P < 0.001) were found in the conventional PEEP group, indicating increased lung aeration loss. In the conventional PEEP group, the intraoperative PaO2/FiO2 ratios were significantly lower but not the postoperative ratios. The PPC incidence was not significantly different between the groups. Post-hoc analysis showed the increase in lung aeration loss and deterioration of intraoperative oxygenation correlated with the deviation from the individualized PEEP. Conclusions Individualized PEEP appears to protect against lung aeration loss and intraoperative oxygenation deterioration. The advantage was greater in patients whose individualized PEEP deviated more from the conventional PEEP.

STUDY DESIGN: Prospective cohort study. PURPOSE: Evaluation of the clinico-radiological outcome and complications of limited laminectomy and restorative spinoplasty in spinal canal stenosis. OVERVIEW OF LITERATURE: It is critical to achieve adequate spinal decompression, while maintaining spinal stability. METHODS: Forty-four patients with degenerative lumbar canal stenosis underwent limited laminectomy and restorative spinoplasty at our centre from July 2008 to December 2010. Four patients were lost to follow-up leaving a total of 40 patients at an average final follow-up of 32 months (range, 24-41 months). There were 26 females and 14 males. The mean+/-standard deviation (SD) of the age was 64.7+/-7.6 years (range, 55-88 years). The final outcome was assessed using the Japanese Orthopaedic Association (JOA) score. RESULTS: At the time of the final follow-up, all patients recorded marked improvement in their symptoms, with only 2 patients complaining of occasional mild back pain and 1 patient complaining of occasional mild leg pain. The mean+/-SD for the preoperative claudication distance was 95.2+/-62.5 m, which improved to 582+/-147.7 m after the operation, and the preoperative anterio-posterior canal diameter as measured on the computed tomography scan was 8.3+/-2.1 mm, which improved to 13.2+/-1.8 mm postoperatively. The JOA score improved from a mean+/-SD of 13.3+/-4.1 to 22.9+/-4.1 at the time of the final follow-up. As for complications, dural tears occurred in 2 patients, for which repair was performed with no additional treatment needed. CONCLUSIONS: Limited laminectomy and restorative spinoplasty is an efficient surgical procedure which relieves neurogenic claudication by achieving sufficient decompression of the cord with maintenance of spinal stability.
Asian Continental Ancestry Group ; Back Pain ; Cohort Studies ; Constriction, Pathologic* ; Decompression ; Female ; Follow-Up Studies ; Humans ; Laminectomy* ; Leg ; Lost to Follow-Up ; Male ; Prospective Studies ; Spinal Canal*

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.