EAI Endorsed Transactions on Pervasive Health and Technology
EAI Endorsed Transactions on Pervasive Health and Technology is open access, a peer-reviewed scholarly journal focused on personal electronic health assistants, health crowdsourcing, data mining, knowledge management, IT applications to the needs of patients, disease prevention, and awareness, electronic and mobile health platforms including design and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2023, the journal follows a continuous publication model. INDEXING: Scopus (CiteScore: 3.6), Compendex, DOAJ, ProQuest, EBSCO, DBLP
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
Expression of Concern Notice: Analysis of the implementation of teletraining and teleIEC in healthcare services: Case study
We, the publisher, are issuing an expression of concern about Sarita Saavedra et al. (2024) RAnalysis of the implementation of teletraining and teleIEC in healthcare services: Case study. https://doi.org/10.4108/eetpht.10.5057 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Sarita Saavedra, Lloy Pinedo, Tamara Peña https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Evaluation and Monitoring System for Exercise Rehabilitation Based on Combined Chinese and Western Medicine Technology
We, the publisher, are issuing an expression of concern about Yunxiang Shang (2024) Evaluation and Monitoring System for Exercise Rehabilitation Based on Combined Chinese and Western Medicine Technology. https://doi.org/10.4108/eetpht.10.5138 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Yunxiang Shang https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern: Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training
We, the publisher, are issuing an expression of concern about Jie Xu, (2024) Analysis and Improvement of the Application of Playground Sports Posture Detection Technology in Physical Education Teaching and Training. https://doi.org/10.4108/eetpht.10.5161 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Jie Xu https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern: Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm
We, the publisher, are issuing an expression of concern about Binbin Han et al. (2024) Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO Algorithm. https://doi.org/10.4108/eetpht.10.5147 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Binbin Han, Fuliang Zhang, Zhenyun Chang, Fang Feng https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern: Bibliometric analysis of publications on neuroscience and noncommunicable diseases in the Scopus database
We, the publisher, are issuing an expression of concern about Antony Paul Espiritu-Martinez et al., (2024) Bibliometric analysis of publications on neuroscience and noncommunicable diseases in the Scopus database. https://doi.org/10.4108/eetpht.10.5699 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Antony Paul Espiritu-Martinez, Miriam Zulema Espinoza-Veliz, Melvi Janett Espinoza-Egoavil, Katerine Karen Gomez-Perez, Karina Liliana Espinoza-Véliz, Linda Flor Villa-Ricapa, Eva Luisa Núñez-Palacios https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Use of wearable technologies in health promotion in Human Medicine students
We, the publisher, are issuing an expression of concern about Javier Eduardo Curo Yllaconza et al. (2024) Use of wearable technologies in health promotion in Human Medicine students. https://doi.org/10.4108/eetpht.10.5701 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Javier Eduardo Curo Yllaconza, Renee Amparo Valle Elescano, Tania Luz Vilchez Cuevas, Mirelly Velásquez Orellana, José Hugo Tezén Campos, Jaime Salazar Montenegro, Digmer Pablo Riquez Livia, Eleazar Fidel Peralta Loayza https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern: Analysis of the use of electronic medical records and its effect on improving patient care
We, the publisher, are issuing an expression of concern about Lenka Angelita Kolevic Roca et al. (2024) Analysis of the use of electronic medical records and its effect on improving patient care. https://doi.org/10.4108/eetpht.10.5702 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Lenka Angelita Kolevic Roca, Carlos Víctor Mora Aguilar, Rosaria Luz Diaz Ramos, Dimna Zoila Alfaro Quezada, Mirelly Velásquez Orellana https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Status of high-impact scientific publication in nursing in Latin America
We, the publisher, are issuing an expression of concern about Espinoza-Véliz et al. (2024) Status of high-impact scientific publication in nursing in Latin America. https://doi.org/10.4108/eetpht.10.5705 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Miriam Zulema Espinoza-Véliz, Antony Paul Espiritu-Martinez, Melvi Janett Espinoza-Egoavil, Maribel Nerida Usuriaga-Palacios, Enzo Renatto Bazualdo-Fiorini, Jorge Luis Hilario Rivas, David Hugo Bernedo-Moreira https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Individual Intervention and Assessment of Students' Physical Fitness Based on the "Three Precision" Applet and Mixed Strategy Optimised CNN Networks
We, the publisher, are issuing an expression of concern about Daomeng Zhang, (2024) Individual Intervention and Assessment of Students' Physical Fitness Based on the "Three Precision" Applet and Mixed Strategy Optimised CNN Networks. https://doi.org/10.4108/eetpht.10.5852 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Daomeng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Analysis Method of Special Physical Training Mode of Basketball Teams in Colleges Based on WeChat Applet and FTTA Optimised LSTM
We, the publisher, are issuing an expression of concern about Gang Chen, Shuaishuai Zhang, (2024) Analysis Method of Special Physical Training Mode of Basketball Teams in Colleges Based on WeChat Applet and FTTA Optimised LSTM. https://doi.org/10.4108/eetpht.10.5853 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Gang Chen, Shuaishuai Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Thermal image processing system to monitor muscle warm-up in students prior to their sports activities
We, the publisher, are issuing an expression of concern about Naara Medina-Altamirano et al., (2024) Thermal image processing system to monitor muscle warm-up in students prior to their sports activities. https://doi.org/10.4108/eetpht.10.5888 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Naara Medina-Altamirano, Duliano Ramirez-Morales , Darwin Gutierrez-Alamo , Jose Rojas-Diaz, Wilver Ticona-Larico, Cynthia López-Gómez https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status
We, the publisher, are issuing an expression of concern about Yu Li, Yuetong Gao (2024) Research on Portable Intelligent Terminal and APP Application Analysis and Intelligent Monitoring Method of College Students' Health Status. https://doi.org/10.4108/eetpht.10.5899 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Yu Li, Yuetong Gao https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling
We, the publisher, are issuing an expression of concern about Fangming Dai, Zhiyong Li, (2024) Research on 2D Animation Simulation Based on Artificial Intelligence and Biomechanical Modeling. https://doi.org/10.4108/eetpht.10.5907 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Fangming Dai, Zhiyong Li https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes
We, the publisher, are issuing an expression of concern about Shuaishuai Zhang, Gang Chen (2024) Designing and Analysing an APP based on "Internet+" for Integrating Health Data of University Physical Classes. https://doi.org/10.4108/eetpht.10.5856 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Shuaishuai Zhang; Gang Cheng https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Deep learning in sports skill learning: a case study and performance evaluation
We, the publisher, are issuing an expression of concern about Diandong Lian (2024) Deep learning in sports skill learning: a case study and performance evaluation. https://doi.org/10.4108/eetpht.10.5809 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Diandong Lian https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Innovative Application of Computer Vision and Motion Tracking Technology in Sports Training
We, the publisher, are issuing an expression of concern about Changqing Liu, Yanan Xie (2024) Innovative Application of Computer Vision and Motion Tracking Technology in Sports Training. https://doi.org/10.4108/eetpht.10.5763 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Changqing Liu, Yanan Xie https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Wearable Sports Smart Glasses Real-time Monitoring and Feedback Mechanism in Physical Education
We, the publisher, are issuing an expression of concern about Zhongchen Zhang, Xiaomei Wang (2024) Wearable Sports Smart Glasses Real-time Monitoring and Feedback Mechanism in Physical Education.https://doi.org/10.4108/eetpht.10.5531 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Zhongchen Zhang, Xiaomei Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students
We, the publisher, are issuing an expression of concern about Jing Wang (2024) Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students. https://doi.org/10.4108/eetpht.10.5506 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Jing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices
We, the publisher, are issuing an expression of concern about Binbin Han et al. (2024) Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices. https://doi.org/10.4108/eetpht.10.5163 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Bin Bin Han, Fuliang Zhang, Lin Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration
We, the publisher, are issuing an expression of concern about Wei Zhu (2024) Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration. https://doi.org/10.4108/eetpht.10.5152 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Wei Zhu https://creativecommons.org/licenses/by-nc-sa/4.0
Expression of Concern Notice: Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes
We, the publisher, are issuing an expression of concern about Bin Wu (2024) Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes. https://doi.org/10.4108/eetpht.10.5150 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
Copyright (c) 2026 Bin Wu https://creativecommons.org/licenses/by-nc-sa/4.0
Retraction Notice: Intelligent ECG Classification Based on Improved Swin Transformer Model
We, the Publisher, have retracted the following article: Bin Xu et al. (2025). Intelligent ECG Classification Based on Improved Swin Transformer Model. EAI Endorsed Scal Inf Syst. https://doi.org/10.4108/eetpht.11.11675 The article has been retracted due to authorship dispute. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 Bin Xu, Zongzhen Yue, Shoucheng Ji, Ning Sun, Jianyong Zhen https://creativecommons.org/licenses/by-nc-sa/4.0
ECG Signal Classification Method Based on Structural Risk Minimization
Arrhythmia stands as a primary contributor to cardiovascular disease-associated mortality. Therefore, the classification and monitoring of abnormal electrocardiogram (ECG) signals are of paramount importance for preventive purposes. Although deep - learning - based ECG classification methods have yielded promising outcomes, they frequently encounter challenges in optimizing performance across diverse patient datasets. To overcome these limitations, this research endeavors to enhance the generalization ability of deep - learning models for ECG signal classification. It achieves this by integrating structural risk minimization principles and incorporating RR interval information into the classification process. A convolutional neural network (CNN) founded on structural risk minimization is proposed. Instead of employing the traditional cross-entropy loss, this study adopts a loss function inspired by support vector machine (SVM) classifiers to optimize the CNN. Moreover, the RR interval information, which is often lost during beat segmentation, is manually extracted and integrated into the CNN network to improve classification accuracy. The proposed method attains an accuracy, specificity, and sensitivity of 88.2% respectively, demonstrating superior performance when compared to traditional and existing methods. This improvement underscores the efficacy of the structural risk minimization approach and the integration of RR interval information in enhancing the model's generalization across patient datasets. The method's convenience and effectiveness render it particularly well-suited for real-time application in wearable devices, facilitating the early detection of abnormal ECG patterns and potentially preventing cardiovascular disease-related fatalities.
Copyright (c) 2026 Feng Hong, Hengyang Fang, Ruru Liu https://creativecommons.org/licenses/by-nc-sa/4.0
RETRACTED: Intelligent ECG Classification Based on Improved Swin Transformer Model
RETRACTED: The article has been retracted due to authorship dispute. The retraction notice can be found here: https://doi.org/10.4108/eetpht.11.12296
Copyright (c) 2026 Bin Xu, Zongzhen Yue, Shoucheng Ji, Ning Sun, Jianyong Zheng https://creativecommons.org/licenses/by-nc-sa/4.0
Biomorphic Design of Age-Friendly Wearable Devices: Flexible Circuits and Self-Luminescent Display Interfaces Based on Vascular Bionic Structures
With the accelerating aging of the global population, the importance of health monitoring and assistive technologies for the elderly is becoming increasingly prominent. This study aims to propose and validate a biomorphic design framework based on vascular biomimetic structures to address the challenges of mechanical adaptability, signal stability, and cognitive interaction in age-friendly wearable devices. Using microfluidic lithography and differential capillary self-assembly, a liquid metal flexible circuit with a fractal topology mimicking human microvasculature is constructed on a SEBS/TPU composite substrate. A biomimetic microhinge array is used to disperse stress, and micropits are laser-engraved on the surface to enhance breathability. The display interface, based on a poly(3,4-ethylenedioxythiophene) microcapsule array, combines electrochromic and photoluminescent effects to map parameters such as blood pressure and blood oxygen into dynamic fractal growth patterns. Experimental data show that the biomimetic circuit exhibits a resistance change of only 11.72% under a strain of only 15%, and the substrate stiffness pressure is 0.82±0.07 kPa, ensuring signal stability and comfortable wear during exercise. The user experience is significantly improved, with a low discomfort feedback rate of 7.2%. The conclusion shows that biomorphic design based on vascular bionic structure provides a suitable and systematic solution for the physiological adaptation and cognitive interaction of elderly-friendly wearable devices.
Copyright (c) 2026 Yingyu Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Implementation of Intelligent Treatment Plan Recommendation System Based on Big Data of Orthodontic Cases
INTRODUCTION: Contemporary orthodontic treatment planning relies heavily on individual practitioner experience, leading to significant variability in clinical decisions for similar malocclusion presentations and limiting standardized evidence-based care. OBJECTIVES: This research aimed to develop an intelligent treatment recommendation system integrating medical big data analytics with specialized orthodontic knowledge extraction to enhance clinical decision-making accuracy and efficiency. METHODS: The study integrated 1,106 cases from multiple public orthodontic datasets, including ISBI 2015 Grand Challenge, GitHub repositories, PubMed Central case reports, and Kaggle dental imaging competitions. Graph Attention Networks were applied alongside collaborative filtering methods to process these cases and construct orthodontic knowledge graphs that map diagnostic data to treatment outcomes. RESULTS: When tested on extraction decisions, the hybrid system correctly identified treatment needs in 94.2% of cases, while manual evaluation achieved 78.8% accuracy. Processing required only 2.3±0.4 seconds, compared to 35-45 minutes for traditional cephalometric analysis. Different malocclusion categories showed varying results, with Class I cases reaching 96.5% accuracy and Class II Division 2 cases achieving 91.2%. Processing speed improved by 99.8%, sensitivity increased 24.7%, and clinical reliability improved by 28.3% compared to standard diagnostic procedures. CONCLUSION: Big data analytics can enhance orthodontic decision-making while preserving the personalized treatment planning that remains fundamental to achieving optimal treatment outcomes.
Copyright (c) 2026 Yuzhe Huang, Rasheed Abdulsalam https://creativecommons.org/licenses/by-nc-sa/4.0
An Interpretable Stacking Ensemble Model with SHAP for Geriatric Depression Prediction: Analysis of the NHANES 2005-2023 Database
OBJECTIVE: Leveraging multimodal data from the 2005-2023 National Health and Nutrition Examination Survey (NHANES) database, this study aims to develop a predictive method for the geriatric depression that combines high predictive accuracy with good interpretability, thereby providing support for in-depth exploration of the pathogenesis and risk factors of geriatric depression. METHODS: Data from 8760 participants aged 65 and older in the NHANES database from 2005-2023 are utilized to develop and validate the stacking ensemble predictive model. Depression is assessed using the Patient Health Questionnaire-9 (PHQ-9) total score meeting or exceeding 10. Before the model construction, this work employs the normalization of training data and test data, Synthetic Minority Over-sampling Technique - Random Under-Sampling (SMOTE-RUS) hybrid sampling strategy to address the class imbalance, and the recursive feature elimination method based on the random forest (RFE-RF) for feature selection. A stacking ensemble predictive framework for depression is constructed based on the primary learners (Random Forest, SVM, XGBoost, and Logistic Regression) and meta-learners (SVM and Logistic Regression). Finally, the interpretable machine learning technique SHapley Additive exPlanations (SHAP) is used to visualize the model predictive outputs. RESULTS: The XGBoost model demonstrated outstanding performance on the test set in terms of AUC (83.92%), while the Random Forest (RF) model excelled in sensitivity (71.05%). Subsequently, a specifically designed RFE-Stacking ensemble model, using RF and XGBoost as the primary learner and the SVM as the meta-learner, is developed. In comparison, this stacking ensemble model exhibits the best predictive performance with the biggest AUC (85.14%) and the highest sensitivity (78.71%). The SHAP interpretation reveals that general health condition, frequency of oral pain in the past year, marital status, history of mental health consultations in the past year, and frequency of urine leakage are the top five most influential factors in predicting the depression risk. CONCLUSION: This stacking ensemble model enhances the performance of both the primary learners and the meta-learners. This verifies the feasibility and effectiveness of the proposed model in predicting the geriatric depression. This work integrating the stacking ensemble model with SHAP offers valuable clinical references for assessing the risk of depressive symptoms, which is beneficial to develop the personalized depression interventions and preventions in the elderly.
Copyright (c) 2026 Manman Cui, Xian Li, Wei Gong https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Medical Question-Answering Systems with Knowledge Graph-Integrated Large Language Models: A Comparative Analysis
This study investigates the impact of integrating knowledge graph prompt engineering (KGPE) with large language models in the context of medical question answering. The Hugging Face MedQA dataset (N = 5,000) was utilised for the extraction of key medical entities via the implementation of named entity recognition, and the construction of SPARQL-based relational prompts from the knowledge base of Wikipedia to guide the reasoning process. Two models, Llama-2-7B-chat-hf and Qwen-2-7B-Instruct, are evaluated through a weighted aggregation of BLEU, ROUGE, and cosine similarity metrics. The findings demonstrate that Qwen-2-7B-Instruct attains substantial enhancements under KGPE—BLEU escalating from 0.366 to 0.531 (+0.165) and cosine similarity rising from 0.763 to 0.820 (+0.057). Conversely, Llama-2-7B-chat-hf exhibits a modest decrease, signifying divergent responsiveness to structured knowledge. These findings demonstrate that integrating structured knowledge through KGPE enhances factual accuracy and semantic coherence in medical reasoning without modifying model architecture
Copyright (c) 2026 Jiaxu Lin, Silin Ouyang https://creativecommons.org/licenses/by-nc-sa/4.0
RAG-TPC: Retrieval Augmented Generation for Teenager Psychological Counseling Using DeepSeek
The rising prevalence of adolescent mental health issues underscores the limitations of traditional counselling services in terms of scalability, timeliness, and accessibility. This paper presents RAG-TPC, a Retrieval- Augmented Generation framework built upon the DeepSeek language model for teenage psychological counselling. The system incorporates intent classification, semantic retrieval, and structured prompt-based generation to produce safe, empathetic, and contextually appropriate responses. We construct a domain- specific dataset spanning general distress, mental illness, and SOS emergencies, and employ LoRA-based fine-tuning to enhance intent recognition. Experimental results show that RAG-TPC consistently outperforms competitive LLMs in both classification and response quality. Evaluations by psychological professionals further validate the system’s practical effectiveness and ethical reliability, highlighting its potential for scalable AI-assisted mental health support.
Copyright (c) 2026 Yanling Li, Yibo Gao, Fangying Quan, Xudong Luo https://creativecommons.org/licenses/by-nc-sa/4.0
Fusion of Radiomics and Deep Learning Features for Enhanced Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer
INTRODUCTION: Pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) is a validated surrogate endpoint for long-term survival in breast cancer patients. However, conventional biomarkers exhibit limited predictive accuracy, with approximately 60-80% of patients failing to achieve pCR. Dynamic contrast-enhanced MRI (DCE-MRI) provides high-resolution information on tumor vascularization and heterogeneity, but prior radiomics models have predominantly relied on single-feature paradigms, which may not fully capture complex tumor phenotypes. METHODS: We developed a multimodal deep-learning radiomics (DLR) pipeline using the publicly available ACRIN 6657/I-SPY1 dataset (n=163). After rigorous preprocessing (bias-field correction, isotropic resampling, Z-score normalization), we extracted a comprehensive set of 1,702 standardized radiomics features compliant with the Image Biomarker Standardization Initiative (IBSI), which quantitatively capture tumor morphology, texture, and intensity patterns. Additionally, 8,576 deep learning features were derived from five convolutional neural networks (ResNet50, DenseNet-169, InceptionV3, InceptionResNetV2, EfficientNetB0), enabling the model to learn complex, data-driven representations beyond human-defined features. The fusion of these complementary feature types provides a more holistic characterization of tumor phenotype, significantly enhancing predictive performance compared to single-modality approaches. A two-stage feature-selection strategy utilizing univariate analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was applied, followed by linear signature construction. Ten classifiers were evaluated under stratified cross-validation and independent testing. RESULTS: The fusion of handcrafted radiomics and deep learning features significantly enhanced predictive performance. The best-performing model, a multilayer perceptron (MLP), achieved an area under the receiver operating characteristic curve (AUC) of 0.98 on the independent test set, with an accuracy of 95.92%, sensitivity of 92.86%, and specificity of 97.14%. Logistic regression also demonstrated strong performance (AUC = 0.980). Decision curve analysis confirmed the clinical utility of all models across a wide range of threshold probabilities. CONCLUSIONS: The integration of radiomics and deep learning features within a machine learning framework provides a robust, non-invasive tool for predicting pCR to NAC in breast cancer. This multimodal approach outperforms single-modality models and offers potential for clinical translation to personalize treatment strategies and avoid ineffective chemotherapy. Further multi-center validation is warranted to confirm its generalizability.
Copyright (c) 2026 Xupeng Lu, Hazirah Bee bt Yusof Ali, Junxiu Wang https://creativecommons.org/licenses/by-nc-sa/4.0