A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture.

The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist fracture on medical imaging can have significant consequences for patients, resulting in delayed treatment and poor functional recovery. Therefore, an intelligent method is needed in the medical department to precisely diagnose wrist fracture via an automated diagnosing tool by considering it a second option for doctors. In this research, a fused model of the deep learning method, a convolutional neural network (CNN), and long short-term memory (LSTM) is proposed to detect wrist fractures from X-ray images. It gives a second option to doctors to diagnose wrist facture using the computer vision method to lessen the number of missed fractures. The dataset acquired from Mendeley comprises 192 wrist X-ray images. In this framework, image pre-processing is applied, then the data augmentation approach is used to solve the class imbalance problem by generating rotated oversamples of images for minority classes during the training process, and pre-processed images and augmented normalized images are fed into a 28-layer dilated CNN (DCNN) to extract deep valuable features. Deep features are then fed to the proposed LSTM network to distinguish wrist fractures from normal ones. The experimental results of the DCNN-LSTM with and without augmentation is compared with other deep learning models. The proposed work is also compared to existing algorithms in terms of accuracy, sensitivity, specificity, precision, the F1-score, and kappa. The results show that the DCNN-LSTM fusion achieves higher accuracy and has high potential for medical applications to use as a second option.

Advances in the Diagnosis and Management of Neonatal Sarcomas.

Neonatal sarcomas comprise a heterogeneous group of rare soft tissue neoplasms that present unique diagnostic and therapeutic challenges. Recent advances in molecular profiling have improved diagnostic capabilities and reveal novel therapeutic targets. Clinical trials demonstrate differences in behavior between sarcoma subtypes that allow for better clinical management. Surgical resection has been replaced with a multimodal approach that includes chemotherapy and radiotherapy. Despite these advances, neonates with sarcoma continue to fare worse than histologically similar sarcomas in older children, likely reflecting differences in tumor biology and the complexities of neonatal medicine. This review focuses on recent advances in managing neonatal sarcomas.

More Than Meets the Eye? A Cautionary Tale of Malignant Ectomesenchymoma Treated as Low-risk Orbital Rhabdomyosarcoma.

Malignant ectomesenchymoma (MEM) is a rare multiphenotypic tumor comprised of mesenchymal and neuroectodermal components. MEM is typically diagnosed in infants and younger children and outcomes are variable. The current approach for treating MEM includes targeting the more aggressive mesenchymal component of the tumor, which is often rhabdomyosarcoma. Here, we describe a case of an orbital tumor initially diagnosed and treated as low-risk rhabdomyosarcoma. Local failure prompting a second biopsy revealed neuronal differentiation consistent with a diagnosis of MEM. Intensifying therapy and local radiotherapy led to a long-term cure. This case offers a cautionary tale that while outcomes for MEM were similar to matched rhabdomyosarcoma cohorts when treated on conventional Intergroup Rhabdomyosarcoma Study Group (IRSG) III/IV protocols, treating MEM using a decreased intensity low-risk rhabdomyosarcoma regimen may not be sufficient.