Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients.
Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients.
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DOI：
10.3389/fphys.2021.745635
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发表时间：
2021
影响因子：
4
通讯作者：
Kociński J
中科院分区：
文献类型：
--
作者：
Hafke-Dys H;Kuźnar-Kamińska B;Grzywalski T;Maciaszek A;Szarzyński K;Kociński J
背景：有效和可靠的监测哮喘在家里是一个相关的因素，可能会减少需要咨询医生的人。目的：我们分析了基于人工智能（AI）分析标准听诊器听诊期间记录的声音来确定病理性呼吸现象强度的可能性。方法：从899例患者中收集了包括1，043次听诊检查（9，319次记录）的评估集。将检查分为四组：哮喘伴和不伴异常声音（分别为AA和AN），非哮喘伴和不伴异常声音（分别为NA和NN）。异常声音的存在由对AI预测不知情的3名医生组成的小组进行评估。AI在一组独立的9，847个记录上进行训练，以确定喘息、干啰音、细和粗爆裂音及其组合的强度评分（指数）：连续现象（喘息+干啰音）和所有现象。使用基于ROC曲线下面积（AUC）的检查组配对比较来评价每个指标在组间区分中的性能。结果：连续现象指数（AUC 0.94）对AA和AN的分离效果最好，而对NN和NA的分离效果最好。所有现象指数（AUC 0.91）显示出最佳性能。与NA相比，AA显示喘息的患病率略高。结论：结果表明，人工智能的高效率区分哮喘患者的正常和异常的声音，因此这种方法具有很大的潜力，可用于监测哮喘症状在家里。
Background: Effective and reliable monitoring of asthma at home is a relevant factor that may reduce the need to consult a doctor in person. Aim: We analyzed the possibility to determine intensities of pathological breath phenomena based on artificial intelligence (AI) analysis of sounds recorded during standard stethoscope auscultation. Methods: The evaluation set comprising 1,043 auscultation examinations (9,319 recordings) was collected from 899 patients. Examinations were assigned to one of four groups: asthma with and without abnormal sounds (AA and AN, respectively), no-asthma with and without abnormal sounds (NA and NN, respectively). Presence of abnormal sounds was evaluated by a panel of 3 physicians that were blinded to the AI predictions. AI was trained on an independent set of 9,847 recordings to determine intensity scores (indexes) of wheezes, rhonchi, fine and coarse crackles and their combinations: continuous phenomena (wheezes + rhonchi) and all phenomena. The pair-comparison of groups of examinations based on Area Under ROC-Curve (AUC) was used to evaluate the performance of each index in discrimination between groups. Results: Best performance in separation between AA and AN was observed with Continuous Phenomena Index (AUC 0.94) while for NN and NA. All Phenomena Index (AUC 0.91) showed the best performance. AA showed slightly higher prevalence of wheezes compared to NA. Conclusions: The results showed a high efficiency of the AI to discriminate between the asthma patients with normal and abnormal sounds, thus this approach has a great potential and can be used to monitor asthma symptoms at home.
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DOI：
10.1590/s0100-879x2009000700013
发表时间：
2009-07-01
影响因子：
2.3
作者：
Riella, R.J.;Nohama, P.;Maia, J.M.
通讯作者：
Maia, J.M.
影响因子：
8
作者：
Bradley, AP
通讯作者：
Bradley, AP
影响因子：
--
作者：
Nunes C;Pereira AM;Morais-Almeida M
通讯作者：
Morais-Almeida M
DOI：
10.1093/jamia/ocaa072
发表时间：
2020-07-01
影响因子：
6.4
作者：
Mann, Devin M.;Chen, Ji;Nov, Oded
通讯作者：
Nov, Oded
影响因子：
3.7
作者：
Hafke-Dys, Honorata;Breborowicz, Anna;Biniakowski, Adam
通讯作者：
Biniakowski, Adam