Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer.

Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.

Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax.

Objective.The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as 'black boxes' due to their lack of interpretability. This situation presents challenges in identifying reasons for misdiagnoses and improving accuracy, which leads to potential risks of misdiagnosis and delayed treatment. Therefore, how to enhance the interpretability of diagnostic models is crucial for improving patient outcomes and reducing treatment delays. So far, only limited researches exist on deep learning-based prediction of spontaneous pneumothorax, a pulmonary disease that affects lung ventilation and venous return.Approach.This study develops an integrated medical image analysis system using explainable deep learning model for image recognition and visualization to achieve an interpretable automatic diagnosis process.Main results.The system achieves an impressive 95.56% accuracy in pneumothorax classification, which emphasizes the significance of the blood vessel penetration defect in clinical judgment.Significance.This would lead to improve model trustworthiness, reduce uncertainty, and accurate diagnosis of various lung diseases, which results in better medical outcomes for patients and better utilization of medical resources. Future research can focus on implementing new deep learning models to detect and diagnose other lung diseases that can enhance the generalizability of this system.

Photoacoustic method for measuring the elasticity of polydimethylsiloxane at various mixing ratios.

Measuring elasticity without physical contact is challenging, as current methods often require deconstruction of the test sample. This study addresses this challenge by proposing and testing a photoacoustic effect-based method for measuring the elasticity of polydimethylsiloxane (PDMS) at various mixing ratios, which may be applied on the wide range of applications such as biomedical and optical fields. A dual-light laser source of the photoacoustic (PA) system is designed, employing cross-correlation signal processing techniques. The platform systems and a mathematical model for performing PDMS elasticity measurements are constructed. During elasticity detection, photoacoustic signal features, influenced by hardness and shapes, are analyzed using cross-correlation calculations and phase difference detection. Results from phantom tests demonstrate the potential of predicting Young's modulus using the cross-correlation method, aligning with the American Society for Testing and Materials (ASTM) standard samples. However, accuracy may be affected by mixed materials and short tubes. Normalization or calibration of signals is suggested for aligning with Young's coefficient.

Aibika Flower Flavonoid Extract Exhibits Antiulcer Activity in a Murine Model of Ethanol-Induced Acute Gastric Injury.

Aibika (Abelmoschus manihot (L.) Medic) is a garden vegetable whose flower has been shown to have various bioactivities. This study investigated the protective effect of aibika flower flavonoid extract (AFF) on ethanol-induced gastric injury in mice. The experimental results showed that pre-feeding 125 and 250 mg AFF/kg BW for 1 week significantly reduced the gastric injury area in the negative control group from 19.2% to 6.7% and 0.6%, respectively. The results of the pathological sections staining also showed that AFF had a protective ability against alcohol-induced injury of gastric tissue and liver tissue. When the mice were exposed to high concentrations of ethanol, AFF pretreatment significantly upregulated the expression of antioxidant enzymes. The pretreatment also promoted the production of the intracellular antioxidant, reduced glutathione, in both gastric tissue and serum. On the contrary, AFF delayed the lipid peroxidation process, which, in turn, reduced the damage to the gastric mucosa. When acute inflammation was induced by ethanol stimulation, AFF significantly downregulated the proinflammatory cytokines and mediators such as TNF-α, IL-1ß, IL-6, NF-κB, COX-2, and iNOS. Furthermore, AFF pretreatment greatly promoted the production of healing factors, such as matrix metalloproteinase (MMP)-2, MMP-7, and MMP-9, in the gastric tissue. In addition, AFF significantly reduced gastric cell apoptosis induced by ethanol stimulation. These results demonstrate that AFF has a good protective effect on alcohol-induced gastric ulcer and has the potential to be used in gastrointestinal health care.

Radiographic chest wall abnormalities in primary spontaneous pneumothorax identified by artificial intelligence.

Primary spontaneous pneumothorax (PSP) primarily affects slim and tall young males. Exploring the etiological link between chest wall structural characteristics and PSP is crucial for advancing treatment methods. In this case-control study, chest computed tomography (CT) images from patients undergoing thoracic surgery, with or without PSP, were analyzed using Artificial Intelligence. Convolutional Neural Network (CNN) model of EfficientNetB3 and InceptionV3 were used with transfer learning on the Imagenet to compare the images of both groups. A heatmap was created on the chest CT scans to enhance interoperability, and the scale-invariant feature transform (SIFT) was adopted to further compare the image level. A total of 2,312 CT images of 26 non-PSP patients and 1,122 CT images of 26 PSP patients were selected. Chest-wall apex pit (CAP) was found in 25 PSP and three non-PSP patients (p < 0.001). The CNN achieved a testing accuracy of 93.47 % in distinguishing PSP from non-PSP based on chest wall features by identifying the existence of CAP. Heatmap analysis demonstrated CNN's precision in targeting the upper chest wall, accurately identifying CAP without undue influence from similar structures, or inappropriately expanding or minimizing the test area. SIFT results indicated a 10.55 % higher mean similarity within the groups compared to between PSP and non-PSP (p < 0.001). In conclusion, distinctive radiographic chest wall configurations were observed in PSP patients, with CAP potentially serving as an etiological factor linked to PSP. This study accentuates the potential of AI-assisted analysis in refining diagnostic approaches and treatment strategies for PSP.

A Cost-Effective Model for Predicting Recurrent Gastric Cancer Using Clinical Features.

This study used artificial intelligence techniques to identify clinical cancer biomarkers for recurrent gastric cancer survivors. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random Forest using MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged the synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive exPlanations (SHAPs) for feature importance analysis in this study. Our proposed Random Forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86%, and an F1 of 88.2% on the recurrent category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI (body mass index), and gender; these features significantly affect the prediction model's output and are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

Applying Object Detection and Large Language Model to Establish a Smart Telemedicine Diagnosis System with Chatbot: A Case Study of Pressure Injuries Diagnosis System.

Background: The scarcity of medical resources and personnel has worsened due to COVID-19. Telemedicine faces challenges in assessing wounds without physical examination. Evaluating pressure injuries is time consuming, energy intensive, and inconsistent. Most of today's telemedicine platforms utilize graphical user interfaces with complex operational procedures and limited channels for information dissemination. The study aims to establish a smart telemedicine diagnosis system based on YOLOv7 and large language model. Methods: The YOLOv7 model is trained using a clinical data set, with data augmentation techniques employed to enhance the data set to identify six types of pressure injury images. The established system features a front-end interface that includes responsive web design and a chatbot with ChatGPT, and it is integrated with a database for personal information management. Results: This research provides a practical pressure injury staging classification model with an average F1 score of 0.9238. The system remotely provides real-time accurate diagnoses and prescriptions, guiding patients to seek various medical help levels based on symptom severity. Conclusions: This study establishes a smart telemedicine auxiliary diagnosis system based on the YOLOv7 model, which possesses capabilities for classification and real-time detection. During teleconsultations, it provides immediate and accurate diagnostic information and prescription recommendations and seeks various medical assistance based on the severity of symptoms. Through the setup of a chatbot with ChatGPT, different users can quickly achieve their respective objectives.