COPD is a chronic lung condition characterized by persistent respiratory obstruction and airflow limitation. Early detection and diagnosis are crucial to manage the disease effectively and improve patient outcomes. However, many patients, especially in low- and middle-income countries, are diagnosed late due to limited access to spirometry. This study proposes Copd-ChatGLM, a large language model fine-tuned for COPD diagnosis and management using the RAG framework. The model combines deep learning with clinical data to enhance diagnostic accuracy and provide personalized treatment plans. By integrating LoRA, Copd-ChatGLM fine-tunes the ChatGLM3-6B model efficiently to perform COPD-related tasks with minimal computational resources. Experimental results show that Copd-ChatGLM outperforms traditional classification models and general large language models in accuracy, sensitivity, specificity, and F1 score. This model has become a robust and clinically applicable tool for managing COPD, particularly in resource-limited settings.

Chronic Obstructive Pulmonary Disease (COPD) severely affects people’s health. With this in mind, we propose a novel Multi-Stage Composite Ensemble Learning Framework (MSCELF) that can diagnose COPD without utilising pulmonary function tests data. Our method explores 12 features from the patients’ baseline data, medical history, blood tests, and arterial blood gas analysis. In the first stage of our approach, three different ensemble learning methods are employed. The second stage involves the utilization of two machine learning methods. Finally, the Murphy’s method is integrated in the final stage to combine the outputs, with weights being assigned based on their information quantity and credibility. We evaluate our method on a clinical dataset of 329 patients and show that it outperforms existing methods in terms of accuracy, AUC, sensitivity, specificity, PPV, NPV, and F1 score, which are 0.7980, 0.8082, 0.8551, 0.6835, 0.8570, 0.6531, 0.8560.

Medical imaging research has seen advances in deep learning, but temporal aspects in time-series medical imaging data are often overlooked, leading to diagnostic limitations. This study proposes a spatial-temporal fusion approach for medical image segmentation by integrating a 3D UNet spatial network with a novel temporal network, DTransformer, capable of handling irregularly spaced sequences. The 3D UNet extracts spatial features, while DTransformer processes temporal information with time distance considerations using a novel self-attention mechanism. Experiments on a lung CT dataset show significant segmentation accuracy improvements with the fusion approach. DTransformer proves effective for unequally spaced sequences and boosts performance. And spatial-temporal fusion enhances medical image segmentation. Moveover, DTransformer's ability to manage temporal context and time distance holds promise for various tasks, indicating a new avenue for research.

Accurate segmentation of soft tissue sarcoma in medical images is crucial for effective diagnosis and treatment planning. This study aims to improve soft tissue sarcoma segmentation by proposing a two-layer ensemble model that leverages diverse neural network architectures and a confidence-based integration method. In the first layer, we employ prominent models like CaraNet, AttUNet(M), UNet(S), UNet(M), U Transformer, and Swin UNet for initial segmentation. The second layer utilizes a confidence-based ensemble approach to fuse the outputs of the first layer models, enhancing segmentation accuracy. Experimental evaluation on axial and coronal datasets reveals substantial enhancements in Dice coefficient, sensitivity, and specificity, highlighting the effectiveness of our ensemble strategy. Our approach outperforms individual base learners and ensures smoother and more accurate soft tissue sarcoma segmentation. The proposed two-layer ensemble model, integrating diverse neural network models and a confidence-based ensemble technique, offers improved soft tissue sarcoma segmentation results. This approach holds promise for enhancing diagnostic accuracy and aiding medical decision-making in sarcoma cases.

Soft tissues sarcomas include striated muscle, fibrous tissue, fat, and other soft tissues. Simultaneously, their mortality rates are comparable to those of esophageal cancer, cervical cancer, and other cancers. Prior to surgical resection of patients, it is frequently necessary to study and diagnose the sarcoma area using MRI images in order to design a better surgical plan. However, artificial approaches for diagnosing the sarcoma region are time-consuming and error-prone. While the advent of artificial intelligence allows for computer-assisted diagnosis of the sarcoma region. Nevertheless, there is currently a scarcity of high-quality soft tissue sarcoma imaging data sets in relevant sectors. Thus, with the aim to investigate how to use multi-modal MRI images of patients with soft tissue sarcomas to segment the sarcoma area, we collect and process 15372 multi-modal MRI images in coronal of 40 patients with soft tissue sarcomas found in the thigh, which we subsequently combine with the help of several clinicians to mark the sarcoma area. The multi-modal MRI imaging data set of soft tissue sarcoma is therefore acquired by a number of preprocessing techniques. The sarcoma area is then segmented using a multi-encoder and single-decoder network that adapts to multiple input modalities. For motivating the network to learn the important semantic features of different modalities, we design a feature fusion strategy mechanism that is applied to the skip connection. Additionally, self-supervised learning is being investigated to address the issue of a small number of data points in the data set. Experiments show that our network can achieve the highest Dice score of 57.76% on our data set. Our code and the dataset are available at https://github.com/syaxx0819/The-Multimodal-Soft-Tissue-Sarcoma-Image-Dataset.