Material extrusion 3D-printing technology: A new strategy for constructing water-soluble, high-dose, sustained-release drug formulations.

The advantage of low-temperature forming through direct ink writing (DIW) 3D printing is becoming a strategy for the construction of innovative drug delivery systems (DDSs). Optimization of the complex formulation, including factors such as the printing ink, presence of solvents, and potential low mechanical strength, are challenges during process development. This study presents an application of DIW to fabricate water-soluble, high-dose, and sustained-release DDSs. Utilizing poorly compressible metformin hydrochloride as a model drug, a core-shell delivery system was developed, featuring a core composed of 96 % drug powder and 4 % binder, with a shell structure serving as a drug-release barrier. This design aligns with the sustained-release profile of traditional processes, achieving a 25.8 % reduction in volume and enhanced mechanical strength. The strategy facilitates sustained release of high-dose water-soluble formulations for over 12 h, potentially improving patient compliance by reducing formulation size. Process optimization and multi-batch flexibility were also explored in this study. Our findings provide a valuable reference for the development of innovative DDSs and 3D-printed drugs.

An Efficient and Rapid Medical Image Segmentation Network.

Accurate medical image segmentation is an essential part of the medical image analysis process that provides detailed quantitative metrics. In recent years, extensions of classical networks such as UNet have achieved state-of-the-art performance on medical image segmentation tasks. However, the high model complexity of these networks limits their applicability to devices with constrained computational resources. To alleviate this problem, we propose a shallow hierarchical Transformer for medical image segmentation, called SHFormer. By decreasing the number of transformer blocks utilized, the model complexity of SHFormer can be reduced to an acceptable level. To improve the learned attention while keeping the structure lightweight, we propose a spatial-channel connection module. This module separately learns attention in the spatial and channel dimensions of the feature while interconnecting them to produce more focused attention. To keep the decoder lightweight, the MLP-D module is proposed to progressively fuse multi-scale features in which channels are aligned using Multi-Layer Perceptron (MLP) and spatial information is fused by convolutional blocks. We first validated the performance of SHFormer on the ISIC-2018 dataset. Compared to the latest network, SHFormer exhibits comparable performance with 15 times fewer parameters, 30 times lower computational complexity and 5 times higher inference efficiency. To test the generalizability of SHFormer, we introduced the polyp dataset for additional testing. SHFormer achieves comparable segmentation accuracy to the latest network while having lower computational overhead.

Extension-contraction transformation network for pancreas segmentation in abdominal CT scans.

Accurate and automatic pancreas segmentation from abdominal computed tomography (CT) scans is crucial for the diagnosis and prognosis of pancreatic diseases. However, the pancreas accounts for a relatively small portion of the scan and presents high anatomical variability and low contrast, making traditional automated segmentation methods fail to generate satisfactory results. In this paper, we propose an extension-contraction transformation network (ECTN) and deploy it into a cascaded two-stage segmentation framework for accurate pancreas segmenting. This model can enhance the perception of 3D context by distinguishing and exploiting the extension and contraction transformation of the pancreas between slices. It consists of an encoder, a segmentation decoder, and an extension-contraction (EC) decoder. The EC decoder is responsible for predicting the inter-slice extension and contraction transformation of the pancreas by feeding the extension and contraction information generated by the segmentation decoder; meanwhile, its output is combined with the output of the segmentation decoder to reconstruct and refine the segmentation results. Quantitative evaluation is performed on NIH Pancreas Segmentation (Pancreas-CT) dataset using 4-fold cross-validation. We obtained average Precision of 86.59±6.14% , Recall of 85.11±5.96%, Dice similarity coefficient (DSC) of 85.58±3.98%. and Jaccard Index (JI) of 74.99±5.86%. The performance of our method outperforms several baseline and state-of-the-art methods.

Classification of Alzheimer's disease in MRI images using knowledge distillation framework: an investigation.

PURPOSE: Computer-aided MRI analysis is helpful for early detection of Alzheimer's disease(AD). Recently, 3D convolutional neural networks(CNN) are widely used to analyse MRI images. However, 3D CNN requires huge memory cost. In this paper, we introduce cascaded CNN and long and short-term memory (LSTM) networks. We also use knowledge distillation to improve the accuracy of the model using small medical image dataset. METHODS: We propose a cascade structure, CNN-LSTM. CNN is used as the function of feature extraction, and LSTM is used as the classifier. In this way, the correlation between different slices can be considered and the calculation cost caused by 3D data can be reduced. To overcome the problem of limited image training data, transfer learning is a more reasonable way of feature extraction. We use the knowledge distillation algorithm to improve the performance of student models for AD diagnosis through a powerful teacher model to guide the work of student models. RESULTS: The accuracy of the proposed model is improved using knowledge distillation. The results show that the accuracy of the student models reached 85.96% after the guidance of the teacher models, an increase by 3.83%. CONCLUSION: We propose cascaded CNN-LSTM to classify 3D ADNI data, and use knowledge distillation to improve the model accuracy when trained with small size dataset. It can process 3D data efficiently as well as reduce the computational cost.

A metric learning-based method using graph neural network for pancreatic cystic neoplasm classification from CTs.

PURPOSE: Pancreatic cystic neoplasms (PCNs) are relatively rare neoplasms and difficult to be classified preoperatively. Ordinary deep learning methods have great potential to provide support for doctors in PCNs classification but require a quantity of labeled samples and exact segmentation of neoplasm. The proposed metric learning-based method using graph neural network (GNN) aims to overcome the limitations brought by small and imbalanced dataset and get fast and accurate PCNs classification result from computed tomography (CT) images. METHODS: The proposed framework applies GNN. GNNs perform well in fusing information and modeling relational data and get better results on dataset with small size. Based on metric learning strategy, model learns distance from the data. The similarity-based algorithm enhances the classification performance, and more characteristic information is found. We use a convolutional neural network (CNN) to extract features from given images. Then GNN is used to find the similarity between each two feature vectors and complete the classification. Several subtasks consisting of randomly selected images are established to improve generalization of the model. The experiments are carried out on the dataset provided by Huashan Hospital. The dataset is labeled by postoperative pathological analysis and contains region of interest (ROI) information calibrated by experts. We set two tasks based on the dataset: benign or malignant diagnosis of PCNs and classification of specific types. RESULTS: Our model shows good performance on the two tasks with accuracies of 88.926% and 74.497%. The comparison of different methods' F1 scores in the benign or malignant diagnosis shows that the proposed GNN-based method effectively reduces the negative impact brought by imbalanced dataset, which is also verified by the macroaverage comparison in the four-class classification task. CONCLUSIONS: Compared with existing models, the proposed GNN-based model shows better performance in terms of imbalanced dataset with small size while reducing labeling cost. The result provides a possibility for its application into the computer-aided diagnosis of PCNs.

Toward automatic quantification of knee osteoarthritis severity using improved Faster R-CNN.

PURPOSE: Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Radiologists usually review knee X-ray images and grade the severity of the impairments according to the Kellgren-Lawrence grading scheme. However, this approach becomes inefficient in hospitals with high throughput as it is time-consuming, tedious and also subjective. This paper introduces a model for automatic diagnosis of knee OA based on an end-to-end deep learning method. METHOD: In order to process the input images with location and classification simultaneously, we use Faster R-CNN as baseline, which consists of region proposal network (RPN) and Fast R-CNN. The RPN is trained to generate region proposals, which contain knee joint and then be used by Fast R-CNN for classification. Due to the localized classification via CNNs, the useless information in X-ray images can be filtered and we can extract clinically relevant features. For the further improvement in the model's performance, we use a novel loss function whose weighting scheme allows us to address the class imbalance. Besides, larger anchors are used to overcome the problem that anchors don't match the object when increasing the input size of X-ray images. RESULT: The performance of the proposed model is thoroughly assessed using various measures. The results show that our adjusted model outperforms the Faster R-CNN, achieving a mean average precision nearly 0.82 with a sensitivity above 78% and a specificity above 94%. It takes 0.33 s to test each image, which achieves a better trade-off between accuracy and speed. CONCLUSION: The proposed end-to-end fully automatic model which is computationally efficient has the potential to achieve the real automatic diagnosis of knee OA and be used as computer-aided diagnosis tools in clinical applications.