Frontiers in digital health最新文献

{"title":"Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey.","authors":"Ming Li, Xiaomin Xiong, Bo Xu","doi":"10.3389/fdgth.2024.1371302","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1371302","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is transforming healthcare, yet little is known about Chinese oncologists' attitudes towards AI. This study investigated oncologists' knowledge, perceptions, and acceptance of AI in China.</p><p><strong>Methods: </strong>A cross-sectional online survey was conducted among 228 oncologists across China. The survey examined demographics, AI exposure, knowledge and attitudes using 5-point Likert scales, and factors influencing AI adoption. Data were analyzed using descriptive statistics and chi-square tests.</p><p><strong>Results: </strong>Respondents showed moderate understanding of AI concepts (mean 3.39/5), with higher knowledge among younger oncologists. Only 12.8% used ChatGPT. Most (74.13%) agreed AI is beneficial and could innovate healthcare, 52.19% respondents expressed trust in AI technology. Acceptance was cautiously optimistic (mean 3.57/5). Younger respondents (∼30) show significantly higher trust (<i>p</i> = 0.004) and acceptance (<i>p</i> = 0.009) of AI compared to older respondents, while trust is significantly higher among those with master's or doctorate vs. bachelor's degrees (<i>p</i> = 0.032), and acceptance is higher for those with prior IT experience (<i>p</i> = 0.035).Key drivers for AI adoption were improving efficiency (85.09%), quality (85.53%), reducing errors (84.65%), and enabling new approaches (73.25%).</p><p><strong>Conclusions: </strong>Chinese oncologists are open to healthcare AI but remain prudently optimistic given limitations. Targeted education, especially for older oncologists, can facilitate AI implementation. AI is largely welcomed for its potential to augment human roles in enhancing efficiency, quality, safety, and innovations in oncology practice.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302505","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":"A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras.","authors":"Okechinyere Achilonu, George Obaido, Blessing Ogbuokiri, Kehinde Aruleba, Eustasius Musenge, June Fabian","doi":"10.3389/fdgth.2024.1427845","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1427845","url":null,"abstract":"<p><strong>Background: </strong>In South Africa, between 1966 and 2014, there were three kidney transplant eras defined by evolving access to certain immunosuppressive therapies defined as <i>Pre-CYA</i> (before availability of cyclosporine), <i>CYA</i> (when cyclosporine became available), and <i>New-Gen</i> (availability of tacrolimus and mycophenolic acid). As such, factors influencing kidney graft failure may vary across these eras. Therefore, evaluating the consistency and reproducibility of models developed to study these variations using machine learning (ML) algorithms could enhance our understanding of post-transplant graft survival dynamics across these three eras.</p><p><strong>Methods: </strong>This study explored the effectiveness of nine ML algorithms in predicting 10-year graft survival across the three eras. We developed and internally validated these algorithms using data spanning the specified eras. The predictive performance of these algorithms was assessed using the area under the curve (AUC) of the receiver operating characteristics curve (ROC), supported by other evaluation metrics. We employed local interpretable model-agnostic explanations to provide detailed interpretations of individual model predictions and used permutation importance to assess global feature importance across each era.</p><p><strong>Results: </strong>Overall, the proportion of graft failure decreased from 41.5% in the <i>Pre-CYA</i> era to 15.1% in the <i>New-Gen</i> era. Our best-performing model across the three eras demonstrated high predictive accuracy. Notably, the ensemble models, particularly the Extra Trees model, emerged as standout performers, consistently achieving high AUC scores of 0.95, 0.95, and 0.97 across the eras. This indicates that the models achieved high consistency and reproducibility in predicting graft survival outcomes. Among the features evaluated, recipient age and donor age were the only features consistently influencing graft failure throughout these eras, while features such as glomerular filtration rate and recipient ethnicity showed high importance in specific eras, resulting in relatively poor historical transportability of the best model.</p><p><strong>Conclusions: </strong>Our study emphasises the significance of analysing post-kidney transplant outcomes and identifying era-specific factors mitigating graft failure. The proposed framework can serve as a foundation for future research and assist physicians in identifying patients at risk of graft failure.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11405382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302503","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":"Smartphone-based drug testing in the hands of patients with substance-use disorder-a usability study.","authors":"Johan Månflod, Tove Gumbel, Maria Winkvist, Markku D Hämäläinen, Karl Andersson","doi":"10.3389/fdgth.2024.1394322","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1394322","url":null,"abstract":"<p><strong>Aim: </strong>A clinical study was performed to test the usability of a smartphone eye-scanning app at a needle exchange facility to detect drug use to support therapy.</p><p><strong>Methods: </strong>The study recruited 24 subjects who visited the facility one to three times, making a total of 40 visits. During each visit the subjects underwent testing for non-convergence (NC), nystagmus (NY), and pupillary light reflex (PLR) using a smartphone-based eHealth system. The collected eye data were transformed into key features that represent eye characteristics. During each visit, a time-line follow-back interview on recent drug use and a usability questionnaire were completed.</p><p><strong>Results: </strong>Technical usability of the smartphone eye-scanning app was good for PLR and NC, where key features were generated in 82%-91% of the cases. For NY, only 60% succeeded due to cognitive problems to follow instructions. In most cases, subjects were under the influence of drugs when participating in the tests, with an average of 2.4 different drugs ingested within the last 24 h. The key features from PLR could distinguish use of opioids from central stimulants. The usability questionnaire results indicate that 23 of the 24 subjects could perform the eye-scanning by themselves after a short training, even when under severe influence of drugs. The caregiver assessed that 20 out of the 24 challenging subjects could potentially perform these tests in an indoors, home-like environment.</p><p><strong>Conclusions: </strong>Smartphone-based eye-scanning is functional in a patient population with heavy drug use, also when under the influence of drugs. The use of central stimulants can be distinguished from the use of opioids.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11402893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302508","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":"Imaging biobanks: operational limits, medical-legal and ethical reflections.","authors":"Emanuele Capasso, Claudia Casella, Mariagrazia Marisei, Mario Tortora, Francesco Briganti, Pierpaolo Di Lorenzo","doi":"10.3389/fdgth.2024.1408619","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1408619","url":null,"abstract":"<p><p>The extraordinary growth of health technologies has determined an increasing interest in biobanks that represent a unique wealth for research, experimentation, and validation of new therapies. \"Human\" biobanks are repositories of various types of human biological samples. Through years the paradigm has shifted from spontaneous collections of biological material all over the world to institutional, organized, and well-structured forms. Imaging biobanks represent a novel field and are defined by European Society of Radiology as: \"organized databases of medical images, and associated imaging biomarkers shared among multiple researchers, linked to other biorepositories\". Modern radiology and nuclear medicine can provide multiple imaging biomarkers, that express the phenotype related to certain diseases, especially in oncology. Imaging biobanks, not a mere catalogue of bioimages associated to clinical data, involve advanced computer technologies to implement the emergent field of radiomics and radiogenomics. Since Europe hosts most of the biobanks, juridical and ethical framework, with a specific referral to Italy, is analyzed. Linking imaging biobanks to traditional ones appears to be a crucial step that needs to be driven by medical imaging community under clear juridical and ethical guidelines.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11391398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302507","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":"Developing and testing a community based, online vs. face-to-face peer led intervention to improve mental well-being in Cambodian adults with physical disabilities.","authors":"Paul Best, Alan Maddock, Nil Ean, Lorna Montgomery, Cherie Armour, Ciaran Mulholland, Carolyn Blair","doi":"10.3389/fdgth.2024.1372062","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1372062","url":null,"abstract":"<p><strong>Background: </strong>Despite growing international attention, there remains an urgent need to develop mental health services within low and middle income countries. The Khmer Rouge period in Cambodia saw the destruction of all health services infrastructure in the 1970s. Consequently, Cambodia has struggled to rebuild both its economy and healthcare system, with the number of qualified mental health clinicians remaining disproportionately low. Resultantly, there is a pressing need to develop low-cost community based alternatives of mental healthcare.</p><p><strong>Methods: </strong>Using a mixed methods design, researchers developed an 8-week peer-led intervention, known as a Friendship Group, for adults with physical disabilities using both face-to-face and online delivery methods. The Wilcoxon Signed-Rank test was used to assess changes in pre-post survey scores and qualitative data was collected in form of five focus groups post intervention.</p><p><strong>Results: </strong>41 participants were allocated across four Friendship groups - two were online and two face-to-face. Attrition rate was 22% post-intervention (<i>n</i> = 32). ITT analyses showed a statistically significant decrease in psychological distress scores [<i>Z</i> = -3.808, <i>p</i> < .001] from pre [Mdn = 20, IQR = 16.5-25.5] to post [Mdn = 16, IQR = 14-18.5] intervention. A Wilcoxon signed-ranks test also showed a statistically significant decrease in PTSD scores [<i>Z</i> = -2.239, <i>p</i> < .025] from pre [Mdn = 4, IQR = 3-5] to post [Mdn = 3, IQR = 2.75-4] intervention. There was also a statistically significant decrease in worry scores [<i>Z</i> = -3.904, <i>p</i> < .001] from pre [Mdn = 5, IQR = 3.5-6.5] to post [Mdn = 3, IQR = 3-4] intervention. There were no significant group differences between the face to face and online groups. A number of interconnected themes emerged from focus group data (<i>n</i> = 5), these included the mental health benefits of Friendship Groups as conceptualised through knowledge acquisition, skill development and peer support.</p><p><strong>Conclusions: </strong>The Friendship group intervention delivered in both online and face-to-face formats appears feasible and acceptable within the Cambodian context. Initial data revealed positive findings in terms of reduction in psychological distress, worry and PTSD symptoms as well increased feeling as calm.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11385004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302506","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":"Acceptability of digital health technologies in early Parkinson's disease: lessons from WATCH-PD.","authors":"T Kangarloo, R D Latzman, J L Adams, R Dorsey, M Kostrzebski, J Severson, D Anderson, F Horak, D Stephenson, J Cosman","doi":"10.3389/fdgth.2024.1435693","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1435693","url":null,"abstract":"<p><strong>Introduction: </strong>Digital health technologies (DHTs) have the potential to alleviate challenges experienced in clinical trials through more objective, naturalistic, and frequent assessments of functioning. However, implementation of DHTs come with their own challenges, including acceptability and ease of use for study participants. In addition to acceptability, it is also important to understand device proficiency in the general population and within patient populations who may be asked to use DHTs for extended periods of time. We thus aimed to provide an overview of participant feedback on acceptability of DHTs, including body-worn sensors used in the clinic and a mobile application used at-home, used throughout the duration of the Wearable Assessments in the Clinic and at Home in Parkinson's Disease (WATCH-PD) study, an observational, longitudinal study looking at disease progression in early Parkinson's Disease (PD).</p><p><strong>Methods: </strong>82 participants with PD and 50 control participants were enrolled at 17 sites throughout the United States and followed for 12 months. We assessed participants' general device proficiency at baseline, using the Mobile Device Proficiency Questionnaire (MDPQ). The mean MDPQ score at Baseline did not significantly differ between PD patients and healthy controls (20.6 [2.91] vs 21.5 [2.94], <i>p</i> = .10).</p><p><strong>Results: </strong>Questionnaire results demonstrated that participants had generally positive views on the comfort and use of the digital technologies throughout the duration of the study, regardless of group.</p><p><strong>Discussion: </strong>This is the first study to evaluate patient feedback and impressions of using technology in a longitudinal observational study in early Parkinson's Disease. Results demonstrate device proficiency and acceptability of various DHTs in people with Parkinson's does not differ from that of neurologically healthy older adults, and, overall, participants had a favorable view of the DHTs deployed in the WATCH-PD study.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11381495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302504","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":"User involvement in digital mental health: approaches, potential and the need for guidelines.","authors":"Sylvie Bernaerts, Tom Van Daele, Christian Korthé Carlsen, Søren Lange Nielsen, Jolanda Schaap, Yvette Roke","doi":"10.3389/fdgth.2024.1440660","DOIUrl":"10.3389/fdgth.2024.1440660","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11374771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141914","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":"Telemedicine in the post-COVID era: balancing accessibility, equity, and sustainability in primary healthcare.","authors":"Waseem Jerjes, Daniel Harding","doi":"10.3389/fdgth.2024.1432871","DOIUrl":"10.3389/fdgth.2024.1432871","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11371753/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134660","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":"Editorial: Current status of and future directions for assessing technology acceptance for digital (mental) health interventions.","authors":"Jennifer Apolinário-Hagen, Giulia Paganin, Silvia Simbula","doi":"10.3389/fdgth.2024.1467297","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1467297","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142127558","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":"Towards an early warning system for monitoring of cancer patients using hybrid interactive machine learning.","authors":"Andreas Trojan, Emanuele Laurenzi, Stephan Jüngling, Sven Roth, Michael Kiessling, Ziad Atassi, Yannick Kadvany, Meinrad Mannhart, Christian Jackisch, Gerd Kullak-Ublick, Hans Friedrich Witschel","doi":"10.3389/fdgth.2024.1443987","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1443987","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The use of smartphone apps in cancer patients undergoing systemic treatment can promote the early detection of symptoms and therapy side effects and may be supported by machine learning (ML) for timely adaptation of therapies and reduction of adverse events and unplanned admissions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to create an Early Warning System (EWS) to predict situations where supportive interventions become necessary to prevent unplanned visits. For this, dynamically collected standardized electronic patient reported outcome (ePRO) data were analyzed in context with the patient's individual journey. Information on well-being, vital parameters, medication, and free text were also considered for establishing a hybrid ML model. The goal was to integrate both the strengths of ML in sifting through large amounts of data and the long-standing experience of human experts. Given the limitations of highly imbalanced datasets (where only very few adverse events are present) and the limitations of humans in overseeing all possible cause of such events, we hypothesize that it should be possible to combine both in order to partially overcome these limitations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The prediction of unplanned visits was achieved by employing a white-box ML algorithm (i.e., rule learner), which learned rules from patient data (i.e., ePROs, vital parameters, free text) that were captured via a medical device smartphone app. Those rules indicated situations where patients experienced unplanned visits and, hence, were captured as alert triggers in the EWS. Each rule was evaluated based on a cost matrix, where false negatives (FNs) have higher costs than false positives (FPs, i.e., false alarms). Rules were then ranked according to the costs and priority was given to the least expensive ones. Finally, the rules with higher priority were reviewed by two oncological experts for plausibility check and for extending them with additional conditions. This hybrid approach comprised the application of a sensitive ML algorithm producing several potentially unreliable, but fully human-interpretable and -modifiable rules, which could then be adjusted by human experts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;From a cohort of 214 patients and more than 16'000 available data entries, the machine-learned rule set achieved a recall of 19% on the entire dataset and a precision of 5%. We compared this performance to a set of conditions that a human expert had defined to predict adverse events. This \"human baseline\" did not discover any of the adverse events recorded in our dataset, i.e., it came with a recall and precision of 0%. Despite more plentiful results were expected by our machine learning approach, the involved medical experts a) had understood and were able to make sense of the rules and b) felt capable to suggest modification to the rules, some of which could potentially increase their precision. Suggested modifications o","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115633","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}