NPJ Digital Medicine最新文献_第7页

A scoping review of reporting gaps in FDA-approved AI medical devices 对 FDA 批准的人工智能医疗器械的报告差距进行范围审查

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-03 DOI: 10.1038/s41746-024-01270-x

Vijaytha Muralidharan, Boluwatife Adeleye Adewale, Caroline J. Huang, Mfon Thelma Nta, Peter Oluwaduyilemi Ademiju, Pirunthan Pathmarajah, Man Kien Hang, Oluwafolajimi Adesanya, Ridwanullah Olamide Abdullateef, Abdulhammed Opeyemi Babatunde, Abdulquddus Ajibade, Sonia Onyeka, Zhou Ran Cai, Roxana Daneshjou, Tobi Olatunji

{"title":"A scoping review of reporting gaps in FDA-approved AI medical devices","authors":"Vijaytha Muralidharan, Boluwatife Adeleye Adewale, Caroline J. Huang, Mfon Thelma Nta, Peter Oluwaduyilemi Ademiju, Pirunthan Pathmarajah, Man Kien Hang, Oluwafolajimi Adesanya, Ridwanullah Olamide Abdullateef, Abdulhammed Opeyemi Babatunde, Abdulquddus Ajibade, Sonia Onyeka, Zhou Ran Cai, Roxana Daneshjou, Tobi Olatunji","doi":"10.1038/s41746-024-01270-x","DOIUrl":"10.1038/s41746-024-01270-x","url":null,"abstract":"Machine learning and artificial intelligence (AI/ML) models in healthcare may exacerbate health biases. Regulatory oversight is critical in evaluating the safety and effectiveness of AI/ML devices in clinical settings. We conducted a scoping review on the 692 FDA-approved AI/ML-enabled medical devices approved from 1995-2023 to examine transparency, safety reporting, and sociodemographic representation. Only 3.6% of approvals reported race/ethnicity, 99.1% provided no socioeconomic data. 81.6% did not report the age of study subjects. Only 46.1% provided comprehensive detailed results of performance studies; only 1.9% included a link to a scientific publication with safety and efficacy data. Only 9.0% contained a prospective study for post-market surveillance. Despite the growing number of market-approved medical devices, our data shows that FDA reporting data remains inconsistent. Demographic and socioeconomic characteristics are underreported, exacerbating the risk of algorithmic bias and health disparity.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-9"},"PeriodicalIF":12.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01270-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs 利用离体人肺X光片的独特特征提高肺移植预后的准确性

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-03 DOI: 10.1038/s41746-024-01260-z

Bonnie T. Chao, Andrew T. Sage, Micheal C. McInnis, Jun Ma, Micah Grubert Van Iderstine, Xuanzi Zhou, Jerome Valero, Marcelo Cypel, Mingyao Liu, Bo Wang, Shaf Keshavjee

{"title":"Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs","authors":"Bonnie T. Chao, Andrew T. Sage, Micheal C. McInnis, Jun Ma, Micah Grubert Van Iderstine, Xuanzi Zhou, Jerome Valero, Marcelo Cypel, Mingyao Liu, Bo Wang, Shaf Keshavjee","doi":"10.1038/s41746-024-01260-z","DOIUrl":"10.1038/s41746-024-01260-z","url":null,"abstract":"Ex vivo lung perfusion (EVLP) enables advanced assessment of human lungs for transplant suitability. We developed a convolutional neural network (CNN)-based approach to analyze the largest cohort of isolated lung radiographs to date. CNNs were trained to process 1300 longitudinal radiographs from n = 650 clinical EVLP cases. Latent features were transformed into principal components (PC) and correlated with known radiographic findings. PCs were combined with physiological data to classify clinical outcomes: (1) recipient time to extubation of <72 h, (2) ≥ 72 h, and (3) lungs unsuitable for transplantation. The top PC was significantly correlated with infiltration (Spearman R: 0·72, p < 0·0001), and adding radiographic PCs significantly improved the discrimination for clinical outcomes (Accuracy: 73 vs 78%, p = 0·014). CNN-derived radiographic lung features therefore add substantial value to the current assessments. This approach can be adopted by EVLP centers worldwide to harness radiographic information without requiring real-time radiological expertise.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-7"},"PeriodicalIF":12.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01260-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Algorithmovigilance, lessons from pharmacovigilance 算法警戒，从药物警戒中汲取经验教训

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-02 DOI: 10.1038/s41746-024-01237-y

Alan Balendran, Mehdi Benchoufi, Theodoros Evgeniou, Philippe Ravaud

引用次数: 0

Machine learning explains response variability of deep brain stimulation on Parkinson’s disease quality of life 机器学习解释了深部脑刺激对帕金森病患者生活质量的影响。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-02 DOI: 10.1038/s41746-024-01253-y

Enrico Ferrea, Farzin Negahbani, Idil Cebi, Daniel Weiss, Alireza Gharabaghi

{"title":"Machine learning explains response variability of deep brain stimulation on Parkinson’s disease quality of life","authors":"Enrico Ferrea, Farzin Negahbani, Idil Cebi, Daniel Weiss, Alireza Gharabaghi","doi":"10.1038/s41746-024-01253-y","DOIUrl":"10.1038/s41746-024-01253-y","url":null,"abstract":"Improving health-related quality of life (QoL) is crucial for managing Parkinson’s disease. However, QoL outcomes after deep brain stimulation (DBS) of the subthalamic nucleus (STN) vary considerably. Current approaches lack integration of demographic, patient-reported, neuroimaging, and neurophysiological data to understand this variability. This study used explainable machine learning to analyze multimodal factors affecting QoL changes, measured by the Parkinson’s Disease Questionnaire (PDQ-39) in 63 patients, and quantified each variable’s contribution. Results showed that preoperative PDQ-39 scores and upper beta band activity (>20 Hz) in the left STN were key predictors of QoL changes. Lower initial QoL burden predicted worsening, while improvement was associated with higher beta activity. Additionally, electrode positions along the superior-inferior axis, especially relative to the z = −7 coordinate in standard space, influenced outcomes, with improved and worsened QoL above and below this marker. This study emphasizes a tailored, data-informed approach to optimize DBS treatment and improve patient QoL.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-11"},"PeriodicalIF":12.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01253-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ethical guidance for reporting and evaluating claims of AI outperforming human doctors 报告和评估人工智能优于人类医生的道德指南

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-02 DOI: 10.1038/s41746-024-01255-w

Jojanneke Drogt, Megan Milota, Anne van den Brink, Karin Jongsma

引用次数: 0

Games Wide Open to athlete partnership in building artificial intelligence systems Games Wide Open 与运动员合作建立人工智能系统

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-01 DOI: 10.1038/s41746-024-01261-y

Yosra Magdi Mekki, Osman Hassan Ahmed, Dyllan Powell, Amy Price, H. Paul Dijkstra

引用次数: 0

“Towards harmonizing assessment and reimbursement of digital medical devices in the EU through mutual learning” "通过相互学习协调欧盟数字医疗设备的评估和报销"。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-10-01 DOI: 10.1038/s41746-024-01263-w

Rosanna Tarricone, Francesco Petracca, Hannah-Marie Weller

引用次数: 0

Overcoming biases of individual level shopping history data in health research 克服健康研究中个人购物史数据的偏差

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-30 DOI: 10.1038/s41746-024-01231-4

Anya Skatova

{"title":"Overcoming biases of individual level shopping history data in health research","authors":"Anya Skatova","doi":"10.1038/s41746-024-01231-4","DOIUrl":"10.1038/s41746-024-01231-4","url":null,"abstract":"Novel sources of population data, especially administrative and medical records, as well as the digital footprints generated through interactions with online services, present a considerable opportunity for advancing health research and policymaking. An illustrative example is shopping history records that can illuminate aspects of population health by scrutinizing extensive sets of everyday choices made in the real world. However, like any dataset, these sources possess specific limitations, including sampling biases, validity issues, and measurement errors. To enhance the applicability and potential of shopping data in health research, we advocate for the integration of individual-level shopping data with external datasets containing rich repositories of longitudinal population cohort studies. This strategic approach holds the promise of devising innovative methodologies to address inherent data limitations and biases. By meticulously documenting biases, establishing validated associations, and discerning patterns within these amalgamated records, researchers can extrapolate their findings to encompass population-wide datasets derived from national supermarket chain. The validation and linkage of population health data with real-world choices pertaining to food, beverages, and over-the-counter medications, such as pain relief, present a significant opportunity to comprehend the impact of these choices and behavioural patterns associated with them on public health.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-5"},"PeriodicalIF":12.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01231-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Talking about diseases; developing a model of patient and public-prioritised disease phenotypes 谈论疾病；建立患者和公众优先考虑的疾病表型模型

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-30 DOI: 10.1038/s41746-024-01257-8

Karin Slater, Paul N. Schofield, James Wright, Paul Clift, Anushka Irani, William Bradlow, Furqan Aziz, Georgios V. Gkoutos

{"title":"Talking about diseases; developing a model of patient and public-prioritised disease phenotypes","authors":"Karin Slater, Paul N. Schofield, James Wright, Paul Clift, Anushka Irani, William Bradlow, Furqan Aziz, Georgios V. Gkoutos","doi":"10.1038/s41746-024-01257-8","DOIUrl":"10.1038/s41746-024-01257-8","url":null,"abstract":"Deep phenotyping describes the use of standardised terminologies to create comprehensive phenotypic descriptions of biomedical phenomena. These characterisations facilitate secondary analysis, evidence synthesis, and practitioner awareness, thereby guiding patient care. The vast majority of this knowledge is derived from sources that describe an academic understanding of disease, including academic literature and experimental databases. Previous work indicates a gulf between the priorities, perspectives, and perceptions held by different healthcare stakeholders. Using social media data, we develop a phenotype model that represents a public perspective on disease and compare this with a model derived from a combination of existing academic phenotype databases. We identified 52,198 positive disease-phenotype associations from social media across 311 diseases. We further identified 24,618 novel phenotype associations not shared by the biomedical and literature-derived phenotype model across 304 diseases, of which we considered 14,531 significant. Manifestations of disease affecting quality of life, and concerning endocrine, digestive, and reproductive diseases were over-represented in the social media phenotype model. An expert clinical review found that social media-derived associations were considered similarly well-established to those derived from literature, and were seen significantly more in patient clinical encounters. The phenotype model recovered from social media presents a significantly different perspective than existing resources derived from biomedical databases and literature, providing a large number of associations novel to the latter dataset. We propose that the integration and interrogation of these public perspectives on the disease can inform clinical awareness, improve secondary analysis, and bridge understanding and priorities across healthcare stakeholders.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-14"},"PeriodicalIF":12.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01257-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models 利用机器学习模型识别不太可能从全膝关节置换术中受益的人群。

IF 12.4 1区医学

NPJ Digital Medicine Pub Date : 2024-09-30 DOI: 10.1038/s41746-024-01265-8

Xiaodi Liu, Yingnan Liu, Mong Li Lee, Wynne Hsu, Ming Han Lincoln Liow

{"title":"Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models","authors":"Xiaodi Liu, Yingnan Liu, Mong Li Lee, Wynne Hsu, Ming Han Lincoln Liow","doi":"10.1038/s41746-024-01265-8","DOIUrl":"10.1038/s41746-024-01265-8","url":null,"abstract":"Identifying and preventing patients who are not likely to benefit long-term from total knee arthroplasty (TKA) would decrease healthcare expenditure significantly. We trained machine learning (ML) models (image-only, clinical-data only, and multimodal) among 5720 knee OA patients to predict postoperative dissatisfaction at 2 years. Dissatisfaction was defined as not achieving a minimal clinically important difference in postoperative Knee Society knee and function scores (KSS), Short Form-36 Health Survey [SF-36, divided into a physical component score (PCS) and mental component score (MCS)], and Oxford Knee Score (OKS). Compared to image-only models, both clinical-data only and multimodal models achieved superior performance at predicting dissatisfaction measured by AUC, clinical-data only model: KSS 0.888 (0.866–0.909), SF-PCS 0.836 (0.812–0.860), SF-MCS 0.833 (0.812–0.854), and OKS 0.806 (0.753–0.859); multimodal model: KSS 0.891 (0.870–0.911), SF-PCS 0.832 (0.808–0.857), SF-MCS 0.835 (0.811–0.856), and OKS 0.816 (0.768–0.863). Our findings highlighted that ML models using clinical or multimodal data were capable to predict post-TKA dissatisfaction.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":"1-8"},"PeriodicalIF":12.4,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01265-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142351024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0