Smart Health最新文献

{"title":"Privacy-preserving polyp segmentation using federated learning with differential privacy","authors":"Md. Mahmodul Hasan ,&nbsp;Mohammad Motiur Rahman","doi":"10.1016/j.smhl.2025.100551","DOIUrl":"10.1016/j.smhl.2025.100551","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Patient privacy is of paramount importance in the medical field, especially as data-driven medical applications gain popularity. The privacy of medical records is increasingly crucial. In this context, data-oriented polyp (a precancerous stage of colon cancer) segmentation is a critical area of ongoing research, aiming to improve automated segmentation. Accurate segmentation is essential for the complete removal of these overgrown cells from the gastrointestinal system. Although large data sets using data-driven algorithms have shown excellent performance in image segmentation, privacy concerns have limited the availability of such datasets for medical image segmentation tasks, including polyp segmentation. This research aims to develop an approach for polyp segmentation that combines data from multiple sources without compromising patient privacy.</div></div><div><h3>Methods:</h3><div>We design a differentially private federated learning system to segment polyps without compromising privacy. Our approach employs the encoder–decoder architecture UNet 3+ with a deep supervision technique to achieve effective segmentation of polyps in a federated setup. The federated training process aims to find generalized global models for the entities participating in the federation. The study uses four public databases to train and evaluate the proposed method.</div></div><div><h3>Results:</h3><div>The proposed privacy-protected technique demonstrates promising outcomes in polyp segmentation, achieving an average Intersection over Union (IoU) score of 0.90881 ± 0.00355 over four publicly available datasets. Evaluation metrics include precision, sensitivity, and specificity values, indicating the effectiveness of our approach in accurately segmenting polyps.</div></div><div><h3>Conclusions:</h3><div>Our differentially private federated learning system successfully segments polyps without compromising patient privacy. The promising results suggest that this approach can significantly contribute to the field of polyp segmentation, facilitating the use of large datasets while maintaining strict privacy standards.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100551"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic fog computing for enhanced LLM execution in medical applications","authors":"Philipp Zagar ,&nbsp;Vishnu Ravi ,&nbsp;Lauren Aalami ,&nbsp;Stephan Krusche ,&nbsp;Oliver Aalami ,&nbsp;Paul Schmiedmayer","doi":"10.1016/j.smhl.2025.100577","DOIUrl":"10.1016/j.smhl.2025.100577","url":null,"abstract":"<div><div>The ability of large language models (LLMs) to process, interpret, and comprehend vast amounts of heterogeneous data presents a significant opportunity to enhance data-driven care delivery. However, the sensitive nature of protected health information (PHI) raises concerns about data privacy and trust in remote LLM platforms. Additionally, the cost of cloud-based artificial intelligence (AI) services remains a barrier to widespread adoption. To address these challenges, we propose shifting the LLM execution environment from centralized, opaque cloud providers to a decentralized and dynamic fog computing architecture. By running open-weight LLMs in more trusted environments, such as a user’s edge device or a fog layer within a local network, we aim to mitigate the privacy, trust, and financial concerns associated with cloud-based LLMs. We introduce <em>SpeziLLM</em>, an open-source framework designed to streamline LLM execution across multiple layers, facilitating seamless integration into digital health applications. To demonstrate its versatility, we showcase <em>SpeziLLM</em> across six digital health applications, highlighting its broad applicability in various healthcare settings.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100577"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring finetuned audio-LLM on heart murmur features","authors":"Adrian Florea,&nbsp;Xilin Jiang,&nbsp;Nima Mesgarani,&nbsp;Xiaofan Jiang","doi":"10.1016/j.smhl.2025.100557","DOIUrl":"10.1016/j.smhl.2025.100557","url":null,"abstract":"<div><div>Large language models (LLMs) for audio have excelled in recognizing and analyzing human speech, music, and environmental sounds. However, their potential for understanding other types of sounds, particularly biomedical sounds, remains largely underexplored despite significant scientific interest. In this study, we focus on diagnosing cardiovascular diseases using phonocardiograms, i.e., heart sounds. Most existing deep neural network (DNN) paradigms are restricted to heart murmur classification (healthy vs unhealthy) and do not predict other acoustic features of the murmur such as grading, harshness, pitch, and quality, which are important in helping physicians diagnose the underlying heart conditions. We propose to finetune an audio LLM, Qwen2-Audio, on the PhysioNet CirCor DigiScope phonocardiogram (PCG) dataset and evaluate its performance in classifying 11 expert-labeled features. Additionally, we aim to achieve more noise-robust and generalizable system by exploring a preprocessing segmentation algorithm using an audio representation model, SSAMBA. Our results indicate that the LLM-based model outperforms state-of-the-art methods in 10 of the 11 tasks. Moreover, the LLM successfully classifies long-tail features with limited training data, a task that all previous methods have failed to classify. These findings underscore the potential of audio LLMs as assistants to human cardiologists in enhancing heart disease diagnosis.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing polyp detection in endoscopy with cross-channel self-attention fusion","authors":"Xiaolong Liang ,&nbsp;Shuijiao Chen ,&nbsp;Linfeng Shu ,&nbsp;Dechun Wang ,&nbsp;Qilei Chen ,&nbsp;Yu Cao ,&nbsp;Benyuan Liu ,&nbsp;Honggang Zhang ,&nbsp;Xiaowei Liu","doi":"10.1016/j.smhl.2025.100578","DOIUrl":"10.1016/j.smhl.2025.100578","url":null,"abstract":"<div><div>Colorectal cancer (CRC) poses a significant global health challenge, ranking as a leading cause of cancer-related mortality. Colonoscopy, the most effective means of preventing CRC, is utilized for early detection and removal of precancerous growths. However, while there have been many efforts that utilize deep learning based approaches for automatic polyp detection, false positive rates in polyp detection during colonoscopy remain high due to the diverse characteristics of polyps and the presence of various artifacts. This paper introduces an innovative technique aimed at improving polyp detection accuracy in colonoscopy video frames. The proposed method introduces a novel framework incorporating a cross-channel self-attention fusion unit, aimed at enhancing polyp detection accuracy in endoscopic procedures. The integration of this unit proves to play an important role in refining prediction quality, resulting in more precise detection outcomes in complex medical imaging scenarios. To substantiate the effectiveness of our framework, we create an extensive private dataset comprising complete endoscopy videos, captured from diverse equipment from different manufacturers. This dataset represents realistic and intricate application scenarios, offering an authentic and effective foundation for both training and evaluating our framework. Thorough experiments and ablation studies are conducted to assess the performance of our proposed approach. The results demonstrate that our framework, featuring key technical innovations, significantly reduces false detections and achieves a higher recall rate. This underscores the remarkable effectiveness of our framework in upgrading polyp detection accuracy in real-world endoscopy procedures.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100578"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart health practices: Strategies to improve healthcare efficiency through digital twin technology","authors":"Md. Armanul Hasan ,&nbsp;Ridwan Mustofa ,&nbsp;Niamat Ullah Ibne Hossain ,&nbsp;Md. Saiful Islam","doi":"10.1016/j.smhl.2025.100541","DOIUrl":"10.1016/j.smhl.2025.100541","url":null,"abstract":"<div><div>A digital twin (DT) is a virtual representation of a real-world object that has dynamic, bidirectional connections between the real-world object and its digital domain. With the advent of Industry 4.0, DT technology was initially applied in the engineering and manufacturing sectors, but recent research indicates DT may also be useful within the healthcare sector. The purpose of this study was to determine the potential applications of DT technology in the healthcare sector and offer suggestions for its effective implementation by healthcare institutions to increase service efficiency. Based on a review of the literature, we developed a model to demonstrate the applications of DTs on public and personal health. A questionnaire with five points Likert scale was then designed based on this model. Data were collected through an online survey conducted with 306 participants. To verify our hypothesized correlations among the constructs, structural equation modeling was used. The findings suggested that explainable artificial intelligence-based early diagnosis, simulation model-based vaccination, artificial intelligence location technology, sensor-based real-time health monitoring, and in silico personalized medicine are potential applications of DT that can increase healthcare efficiency. We also considered the moderating influence of (a) security and privacy and (b) certification and regulatory issues, acknowledging their pivotal roles in ensuring the successful implementation and widespread acceptance of DT technology in the field of healthcare. This study contributes to the body of knowledge in academia and offers useful insights for technologists, policymakers, and healthcare professionals who want to fully utilize DT technology to build an effective healthcare system that can adapt to the changing needs of communities and individuals.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"36 ","pages":"Article 100541"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel rule-based expert system for early diagnosis of bipolar and Major Depressive Disorder","authors":"Mohammad Hossein Zolfagharnasab ,&nbsp;Siavash Damari ,&nbsp;Madjid Soltani ,&nbsp;Artie Ng ,&nbsp;Hengameh Karbalaeipour ,&nbsp;Amin Haghdadi ,&nbsp;Masood Hamed Saghayan ,&nbsp;Farzam Matinfar","doi":"10.1016/j.smhl.2024.100525","DOIUrl":"10.1016/j.smhl.2024.100525","url":null,"abstract":"<div><div>A confident and timely diagnosis of mental illnesses is one of the primary challenges practitioners repeatedly encounter when they start treating new patients. However, diagnosing can quickly become problematic as the subjects expose comparative symptoms among mental illnesses. Due to influencing a broad populace among mental ailments, an adjusted differentiation between Major Depressive Disorder, Mania Bipolar Disorder, Depressive Bipolar Disorder, and ordinary individuals with mild symptoms is one of the critical subjects for community health. This study responded to the described problem by proposing a novel rule-based Expert System, which evaluates the impact of disorder symptoms on the Certainty Factor concerning each mental status. The semantic rules are developed based on the recommendation of experts, and the implementation is carried out using Prolog and <em>C#</em> languages. Furthermore, an easy-to-use user interface is considered to facilitate the system workflow. The consistency of the developed framework is established by performing rigorous tests by expert psychiatrists as well as 120 clinical samples collected from private samples. Based on the results, the current model classifies mental disorder cases with a success rate of 93.33% using only the 17 symptoms specified in the ontology model. Furthermore, a questionnaire that measures user satisfaction after the test also achieves a mean score of 3.56 out of 4, which indicates a high degree of user acceptance. As a result, it is concluded that the current framework is a reliable tool for achieving a solid diagnosis in a shorter period.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100525"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differences in gait parameters between supervised laboratory and unsupervised daily assessments of healthy adults measured with an in-shoe motion sensor system","authors":"Hiroki Shimizu ,&nbsp;Takanobu Saito ,&nbsp;Shione Kashiyama ,&nbsp;Shinichi Kawamoto ,&nbsp;Saori Morino ,&nbsp;Momoko Nagai-Tanima ,&nbsp;Tomoki Aoyama","doi":"10.1016/j.smhl.2024.100526","DOIUrl":"10.1016/j.smhl.2024.100526","url":null,"abstract":"<div><div>This cross-sectional study compared the gait parameters between supervised laboratory and unsupervised daily life assessments in healthy adults. Gait was evaluated in 24 healthy young adults during 72 h of daily life and a 6-min laboratory gait at a comfortable speed. An in-shoe motion sensor system recorded gait data every 2 min, automatically detected stable gait segments by identifying repetitive movement patterns, and calculated the average of three consecutive valid gait cycles during each measurement period. Significant differences were found in walking speed (stride length divided by stride time; laboratory: 4.60 km/h vs. daily-life: 4.38 km/h), maximum (peak) dorsiflexion angle (laboratory: 29.71° vs. daily-life: 26.65°), maximum (peak) plantar flexion angle (laboratory: 74.54° vs. daily-life: 71.91°), roll angle of heel contact (laboratory: 7.46° vs. daily-life: 6.70°), maximum speed during the swing phase (laboratory: 14.49 km/h vs. daily-life: 12.68 km/h), circumduction (lateral displacement during the swing phase; laboratory: 2.68 cm vs. daily-life: 3.69 cm), toe-in/out angle (laboratory: 13.87° vs. daily-life: 15.32°), stance time (laboratory: 0.62 s vs. daily-life: 0.65 s), and pushing time (time between heel leaving and toe leaving the ground; laboratory: 0.20 s vs. daily-life: 0.21 s). The innovative aspect of this study is the comprehensive evaluation of foot-related gait parameters in real-world environments using an in-shoe motion sensor system. This approach provides ecologically valid insights into gait dynamics during daily activities, emphasizing the importance of real-world assessments for accurately evaluating gait performance and predicting adverse events such as falls. Keywords: Gait, Foot, Laboratory, Daily Life, Unsupervised Assessment, Shoe Motion Sensors.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100526"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel EEG feature selection based on hellinger distance for epileptic seizure detection","authors":"Muhammed Sadiq ,&nbsp;Mustafa Noaman Kadhim ,&nbsp;Dhiah Al-Shammary ,&nbsp;Mariofanna Milanova","doi":"10.1016/j.smhl.2024.100536","DOIUrl":"10.1016/j.smhl.2024.100536","url":null,"abstract":"<div><div>This study introduces a novel feature selection method based on Hellinger distance and particle swarm optimization (PSO) for reducing the dimensionality of features in electroencephalogram (EEG) signals and improving epileptic seizure detection accuracy. In the first phase, the Hellinger distance is used as a filter to remove redundant and irrelevant features by calculating the similarity between blocks within the feature, thus reducing the search space for the subsequent second phase. In the second phase, PSO searches the reduced feature space to select the best subset. Recognizing that both classification accuracy and dimensionality play crucial roles in the performance of feature subsets, PSO searches various sets of features (ranging from 410 to 2867 in EEG signals) derived from the first stage using Hellinger distance, rather than searching through the full set of 4047 features, to select the optimal subset. The proposed Hellinger-PSO approach demonstrates significant improvements in classification accuracy across multiple models. Specifically, Logistic Regression (LR) improved from 91% to 95% (4% improvement), Decision Tree (DT) from 95% to 97% (2% improvement), Naive Bayes (NB) from 94% to 99% (5% improvement), and Random Forest (RF) from 96% to 98% (2% improvement) on the Bonn dataset. Additionally, the method reduces dimensionality while maintaining high classification performance. The results validate the efficacy of the Hellinger-PSO technique, which enhances both the accuracy and efficiency of epileptic seizure detection. This approach has the potential to improve diagnostic accuracy in medical settings, aiding in better patient care and more effective clinical decision-making.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100536"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable screening of oral cancer via deep learning and case-based reasoning","authors":"Mario G.C.A. Cimino ,&nbsp;Giuseppina Campisi ,&nbsp;Federico A. Galatolo ,&nbsp;Paolo Neri ,&nbsp;Pietro Tozzo ,&nbsp;Marco Parola ,&nbsp;Gaetano La Mantia ,&nbsp;Olga Di Fede","doi":"10.1016/j.smhl.2024.100538","DOIUrl":"10.1016/j.smhl.2024.100538","url":null,"abstract":"<div><div>Oral Squamous Cell Carcinoma is characterized by significant mortality and morbidity. Dental professionals can play an important role in its early detection, thanks to the availability of embedded smart cameras for oral photos and remote screening supported by Deep Learning (DL). Despite the promising results of DL for automated detection and classification of oral lesions, its effectiveness is based on a clearly defined protocol, on the explainability of results, and on periodic cases collection. This paper proposes a novel method, combining DL and Case-Based Reasoning (CBR), to allow the post-hoc explanation of the system answer. The method uses explainability tools organized in a protocol defined in the Business Process Model and Notation (BPMN) to allow its experimental validation. A redesign of the Faster-R-CNN Feature Pyramid Networks (FPN) + DL architecture is also proposed for lesions detection and classification, fine-tuned on 160 cases belonging to three classes of oral ulcers. The DL system achieves state-of-the-art performance, i.e., 83% detection and 92% classification rate (98% for neoplastic vs. non-neoplastic binary classification). A preliminary experimentation of the protocol involved both resident and specialized doctors over selected difficult cases. The system and cases have been publicly released to foster collaboration between research centers.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100538"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel guidance framework for nasal rapid antigen tests with improved swab keypoint detection","authors":"Matthias Tschöpe,&nbsp;Dennis Schneider,&nbsp;Sungho Suh,&nbsp;Paul Lukowicz","doi":"10.1016/j.smhl.2024.100534","DOIUrl":"10.1016/j.smhl.2024.100534","url":null,"abstract":"<div><div>The global impact of the COVID-19 pandemic has placed an unprecedented burden on healthcare systems. In this paper, we present a novel deep learning-based framework to guide individuals in performing nasal antigen rapid tests, with a particular focus on improving swab keypoint detection. Our system provides real-time feedback to participants on the correct execution of the test and may issue a certificate upon successful completion. While initially developed for COVID-19 antigen rapid tests, our versatile framework extends its applicability to various nasal screening tests, eliminating the need for specific information about the liquid solvent. To implement and evaluate our framework, we curated a comprehensive dataset with rapid test components and trained an object detection model to identify the position and size of all objects in each video frame. Addressing the challenge of swab depth classification, we propose a novel approach to locate and classify crucial swab points by a self-defined decision tree for depth assessment within the nasal cavity. The robustness of the proposed framework is validated with COVID-19 antigen rapid tests from various manufacturers. Experimental results demonstrate the remarkable performance of the framework in classifying the nasal placement of the swab, achieving an <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-Score of 89.78%. Additionally, our framework attains an <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-Score of 99.37% in classifying final test results on the test device.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100534"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}