Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach.

Ameloblastoma (AM), periapical cyst (PC), and chronic suppurative osteomyelitis (CSO) are prevalent maxillofacial diseases with similar imaging characteristics but different treatments, thus making preoperative differential diagnosis crucial. Existing deep learning methods for diagnosis often require manual delineation in tagging the regions of interest (ROIs), which triggers some challenges in practical application. We propose a new model of Wavelet Extraction and Fusion Module with Vision Transformer (WaveletFusion-ViT) for automatic diagnosis using CBCT panoramic images. In this study, 539 samples containing healthy (n = 154), AM (n = 181), PC (n = 102), and CSO (n = 102) were acquired by CBCT for classification, with an additional 2000 healthy samples for pre-training the domain-adaptive network (DAN). The WaveletFusion-ViT model was initialized with pre-trained weights obtained from the DAN and further trained using semi-supervised learning (SSL) methods. After five-fold cross-validation, the model achieved average sensitivity, specificity, accuracy, and AUC scores of 79.60%, 94.48%, 91.47%, and 0.942, respectively. Remarkably, our method achieved 91.47% accuracy using less than 20% labeled samples, surpassing the fully supervised approach's accuracy of 89.05%. Despite these promising results, this study's limitations include a low number of CSO cases and a relatively lower accuracy for this condition, which should be addressed in future research. This research is regarded as an innovative approach as it deviates from the fully supervised learning paradigm typically employed in previous studies. The WaveletFusion-ViT model effectively combines SSL methods to effectively diagnose three types of CBCT panoramic images using only a small portion of labeled data.

Automated Sagittal Skeletal Classification of Children Based on Deep Learning.

Malocclusions are a type of cranio-maxillofacial growth and developmental deformity that occur with high incidence in children. Therefore, a simple and rapid diagnosis of malocclusions would be of great benefit to our future generation. However, the application of deep learning algorithms to the automatic detection of malocclusions in children has not been reported. Therefore, the aim of this study was to develop a deep learning-based method for automatic classification of the sagittal skeletal pattern in children and to validate its performance. This would be the first step in establishing a decision support system for early orthodontic treatment. In this study, four different state-of-the-art (SOTA) models were trained and compared by using 1613 lateral cephalograms, and the best performance model, Densenet-121, was selected was further subsequent validation. Lateral cephalograms and profile photographs were used as the input for the Densenet-121 model, respectively. The models were optimized using transfer learning and data augmentation techniques, and label distribution learning was introduced during model training to address the inevitable label ambiguity between adjacent classes. Five-fold cross-validation was conducted for a comprehensive evaluation of our method. The sensitivity, specificity, and accuracy of the CNN model based on lateral cephalometric radiographs were 83.99, 92.44, and 90.33%, respectively. The accuracy of the model with profile photographs was 83.39%. The accuracy of both CNN models was improved to 91.28 and 83.98%, respectively, while the overfitting decreased after addition of label distribution learning. Previous studies have been based on adult lateral cephalograms. Therefore, our study is novel in using deep learning network architecture with lateral cephalograms and profile photographs obtained from children in order to obtain a high-precision automatic classification of the sagittal skeletal pattern in children.

Acuity-based nurse assignment and patient scheduling in oncology clinics.

The oncology clinics use different nursing care delivery models to provide chemotherapy treatment to cancer patients. Functional and primary care delivery models are the most commonly used methods in the clinics. In functional care delivery model, patients are scheduled for a chemotherapy appointment without considering availabilities of individual nurses, and nurses are assigned to patients according to patient acuities, nursing skill, and patient mix on a given day after the appointment schedule is determined. Patients might be treated by different nurses on different days of their treatment. In primary care delivery model, each patient is assigned to a primary nurse, and the patients are scheduled to be seen by the same nurse every time they come to the clinic for treatment. However, these clinics might experience high variability in daily nurse workload due to treatment protocols that should be followed strictly. In that case, part-time nurses can be utilized to share the excess workload of the primary nurses. The aim of this study is to develop optimization methods to reduce the time spent for nurse assignment and patient scheduling in oncology clinics that use different nursing care delivery models. For the functional delivery model, a multiobjective optimization model with the objectives of minimizing patient waiting times and nurse overtime is proposed to solve the nurse assignment problem. For the primary care delivery model, another multiobjective optimization model with the objectives of minimizing total overtime and total excess workload is proposed to solve the patient scheduling problem. Spreadsheet-based optimization tools are developed for easy implementation. Computational results show that the proposed models provide multiple nondominated solutions, which can be used to determine the optimal staffing levels.

[Effect of biochar addition on soil evaporation.] / 生物炭对土壤水分蒸发的影响.

In order to determine the rational amount of biochar application and its effect on soil hydrological processes in arid area, soil column experiments were conducted in the laboratory using three biochar additions (5%, 10% and 15%) and four different biochar types (d<0.25 mm bamboo charcoal, 0.25 mm