Mayo Clinic Proceedings. Digital health最新文献

Objective

To evaluate the effect of smart device-based telerehabilitation on Vo_2peak in patients after myocardial infarction.

Patients and Methods

This was a pilot, single-center, randomized, cross-over study with a 3-month intervention. One month after myocardial infarction, patients had cardiopulmonary exercise testing and a 6-minute walking test (6MWT) and were randomly assigned 1:1. In the intervention group, patients received a smartwatch to track the recommended number of steps, which was individualized and derived from the 6MWT. A study nurse telemonitored adherence to the recommended number of steps a day. In the control group, 150 minutes a week of moderate-intensity physical activity was recommended. After 3 months study arms were crossed over, and study procedures were repeated after 3 months.

Results

Between June 1, 2019, and February 28, 2023, 64 patients were randomized, of which 61 (aged 51±10 years, 10% women) completed the study. Overall, the smart device-based telerehabilitation led to 2.31 mL/kg/min (95% CI, 1.25-3.37; P<.001) Vo_2peak increase compared with the control treatment. Furthermore, there was a significant effect on weight (−1.50 kg; 95% CI, −0.39 to −2.70), whereas the effect on the 6MWT distance (4.7 m; 95% CI, −11.8 to 21.1) or Kansas City Quality of Life questionnaire score (0.98; 95% CI, −1.38 to 3.35) was not significant.

Conclusion

Smart device-based cardiac rehabilitation may be a promising alternative for patients unable or unwilling to attend in-person cardiac rehabilitation.

Trial Registration

clinicaltrials.gov Identifier: NCT03926312

The global burden of skin diseases affects over 3 billion individuals, posing important public health challenges worldwide, with profound impacts in both high-income and low-income and middle-income countries. These challenges are exacerbated by widespread disparities in access to dermatologic care and the prevalence of misinformation. This article, derived from the Skin and Digital Summit at the International Master Course on Aging Science critically evaluates how digital technologies such as artificial intelligence, teledermatology, and large language models can bridge these access gaps. It explores practical applications and case studies demonstrating the impact of these technologies in various settings, with a particular focus on adapting solutions to meet the diverse needs of low-income and middle-income countries. In addition, the narrative highlights the ongoing conversation within the dermatologic community about the role of digital advances in health care, emphasizing that this discussion is dynamic and the one that is continuously evolving. Dermatologists play an essential role in this transition, integrating digital tools into mainstream care to complement a patient-centered, culturally sensitive approach. The article advocates for a globally coordinated digital response that not only addresses current disparities in skin health care but also promotes equitable access to digital health resources, making dermatologic care more representative of all skin types and accessible worldwide.

Objective

To develop an artificial intelligence deep neural network (AI-DNN) algorithm to analyze 12-lead electrocardiogram (ECG) for detection of acute pulmonary embolism (PE) and PE categories.

Patients and Methods

A cohort of patients seen between January 1, 1999, and December 31, 2020, from across the Mayo Clinic Enterprise with computed tomography pulmonary angiogram (CTPA) and ECG performed ±6 hours was identified. Natural language processing algorithms were applied to radiology reports to determine the diagnosis of acute PE, acute right ventricular strain pulmonary embolism (RVSPE), saddle pulmonary embolism (SADPE), or no PE. Diagnostic performance parameters of the AI-DNN reported were area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

A cohort of patients with CTPA report and ECG consisted of 79,894 patients including 7423 (9.3%) with acute PE, among whom 1138 patients had RVSPE or SADPE. Artificial intelligence deep neural network predicted acute PE with a modest accuracy of AUROC of 0.69 (95% CI, 0.68-0.71), sensitivity of 63.5%, specificity of 64.7%, PPV of 15.6%, and NPV of 94.5%. The AI-DNN prediction using the same algorithm for RVSPE or SADPE was higher (AUROC, 0.84; 95% CI, 0.81-0.86) with a sensitivity of 80.8%, specificity of 64.7.8%, PPV of 3.5%, and NPV of 99.5%.

Conclusion

An AI-based analysis of 12-lead ECG shows modest detection power for acute PE in patients who underwent CTPA, with higher accuracy for high-risk PE. Moreover, with the high NPV, it has the clinical potential to exclude high-risk PE quickly and correctly.

To assess the current real-world applications of machine learning (ML) and artificial intelligence (AI) as functionality of digital behavior change interventions (DBCIs) that influence patient or consumer health behaviors. A scoping review was done across the EMBASE, PsycInfo, PsycNet, PubMed, and Web of Science databases using search terms related to ML/AI, behavioral science, and digital health to find live DBCIs using ML or AI to influence real-world health behaviors in patients or consumers. A total of 32 articles met inclusion criteria. Evidence regarding behavioral domains, target real-world behaviors, and type and purpose of ML and AI used were extracted. The types and quality of research evaluations done on the DBCIs and limitations of the research were also reviewed. Research occurred between October 9, 2023, and January 20, 2024. Twenty-three DBCIs used AI to influence real-world health behaviors. Most common domains were cardiometabolic health (n=5, 21.7%) and lifestyle interventions (n=4, 17.4%). The most common types of ML and AI used were classical ML algorithms (n=10, 43.5%), reinforcement learning (n=8, 34.8%), natural language understanding (n=8, 34.8%), and conversational AI (n=5, 21.7%). Evidence was generally positive, but had limitations such as inability to detect causation, low generalizability, or insufficient study duration to understand long-term outcomes. Despite evidence gaps related to the novelty of the technology, research supports the promise of using AI in DBCIs to manage complex input data and offer personalized, contextualized support for people changing real-world behaviors. Key opportunities are standardizing terminology and improving understanding of what ML and AI are.

This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.