AI-facilitated home monitoring for cystic fibrosis exacerbations across pediatric and adult populations

IF 5.4 2区医学 Q1 RESPIRATORY SYSTEM

Journal of Cystic Fibrosis Pub Date : 2025-03-01 DOI:10.1016/j.jcf.2025.02.011

Henryk Mazurek , Andrzej Emeryk , Kamil Janeczek , Eric Derom , Barbara Kuźnar-Kamińska , Tomasz Grzywalski , Adam Biniakowski , Krzysztof Szarzyński , Anna Pastusiak , Dominika Kaminiarczyk-Pyzałka , Dick Botteldooren , Honorata Hafke-Dys , Jędrzej Kociński

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引用次数: 0

Abstract

Background

AI-aided home stethoscopes offer the opportunity of continuous remote monitoring of cystic fibrosis (CF) patients, reducing the need for clinic visits.

Aim

This study aimed to analyze the possibility of detecting CF pulmonary exacerbations (PEx) at home using an AI-aided stethoscope (AIS).

Materials and Methods

In a six-month study, 129 CF patients (85 children, 44 adults) used AIS for at least weekly self-examinations, recording various parameters: wheezes, rhonchi, crackles intensity, respiratory and heart rate, and inspiration-to-expiration ratio. Health state surveys were also completed. Physicians evaluated 5160 examinations to identify PEx. Machine learning models were trained using those parameters, and AUCs were calculated for PEx detection.

Results

522 self-examinations were diagnosed clinically as exacerbated. AI-aided home stethoscopes detected 415 exacerbated self-examinations (sensitivity 79.5 % at specificity 89.1 %). Among the single-parameter discriminators, coarse crackles intensity exhibited an AUC of 70 % (95% CI: 65–75) for young children, fine crackles intensity demonstrated an AUC of 75 % (95 % CI: 72–78) for older children, and an AUC of 93 % (95 % CI: 92–93) was achieved for adults using fine crackles intensity. The combination of parameters yielded the highest efficacy, with AUC exceeding 83% for objective parameters from the AI module alone and exceeding 90 % when incorporating both objective and subjective parameters across all groups.

Conclusions

The AI-aided home stethoscope has proven to be a reliable tool for detecting PEx with greater accuracy than self-assessment alone. Implementing this technology in healthcare systems has the potential to provide valuable insights for timely intervention and management of PExes.

查看原文本刊更多论文

人工智能促进了儿童和成人人群囊性纤维化恶化的家庭监测。

背景：人工智能辅助家用听诊器为囊性纤维化（CF）患者提供了持续远程监测的机会，减少了门诊就诊的需要。目的：本研究旨在分析使用人工智能辅助听诊器（AIS）在家中检测CF肺恶化（PEx）的可能性。材料和方法：在一项为期六个月的研究中，129例CF患者（85例儿童，44例成人）使用AIS进行至少每周一次的自我检查，记录各种参数：喘息、隆齐、爆裂声强度、呼吸和心率、吸气呼气比。还完成了健康状况调查。医生评估了5160项检查以确定PEx。使用这些参数训练机器学习模型，并计算用于PEx检测的auc。结果：临床诊断自检加重的522例。人工智能辅助家用听诊器检出415例加重自检（敏感性79.5%，特异性89.1%）。在单参数鉴别器中，粗裂纹强度对幼儿的AUC为70% (95% CI: 65-75)，细裂纹强度对较大儿童的AUC为75% (95% CI: 72-78)，细裂纹强度对成人的AUC为93% （95% CI: 92-93）。参数的组合产生了最高的功效，人工智能模块单独的客观参数的AUC超过83%，当综合所有组的客观和主观参数时，AUC超过90%。结论：人工智能辅助家用听诊器已被证明是检测PEx的可靠工具，其准确性高于单独的自我评估。在医疗保健系统中实施这项技术有可能为及时干预和管理pees提供有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Cystic Fibrosis 医学-呼吸系统

CiteScore

10.10

自引率

13.50%

发文量

1361

审稿时长

50 days

期刊介绍： The Journal of Cystic Fibrosis is the official journal of the European Cystic Fibrosis Society. The journal is devoted to promoting the research and treatment of cystic fibrosis. To this end the journal publishes original scientific articles, editorials, case reports, short communications and other information relevant to cystic fibrosis. The journal also publishes news and articles concerning the activities and policies of the ECFS as well as those of other societies related the ECFS.