StethAid: A Digital Auscultation Platform for Pediatrics.
StethAid: A Digital Auscultation Platform for Pediatrics.
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DOI：
10.3390/s23125750
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发表时间：
2023-06-20
期刊：
影响因子：
--
通讯作者：
Shekhar R
中科院分区：
文献类型：
--
作者：
Arjoune Y;Nguyen TN;Salvador T;Telluri A;Schroeder JC;Geggel RL;May JW;Pillai DK;Teach SJ;Patel SJ;Doroshow RW;Shekhar R
(1)背景：对于许多医疗保健提供者来说，掌握听诊是一项挑战。人工智能（AI）驱动的数字支持正在成为帮助解释听诊声音的辅助工具。有一些人工智能增强的数字听诊器存在，但没有一个专用于儿科。我们的目标是为儿科医学开发一个数字听诊平台。(2)研究方法：我们开发了StethAid-一个用于儿科人工智能辅助听诊和远程医疗的数字平台-它由无线数字听诊器、移动的应用程序、定制的患者提供者门户网站和深度学习算法组成。为了验证StethAid平台，我们表征了我们的听诊器并在两个临床应用中使用该平台：（1）Still杂音识别和（2）喘息检测。据我们所知，该平台已在四个儿童医疗中心部署，以构建第一个也是最大的儿科心肺数据集。我们已经使用这些数据集训练和测试了深度学习模型。(3)结果如下：StethAid听诊器的频率响应与市售Eko Core、Thinklabs One和Littman 3200听诊器的频率响应相当。对于79.3%的肺部病例和98.3%的心脏病例，我们的专家医生离线提供的标签与床边使用声学听诊器的提供者的标签一致。我们的深度学习算法在Still杂音识别（灵敏度为91.9%，特异性为92.6%）和喘息检测（灵敏度为83.7%，特异性为84.4%）方面都实现了高灵敏度和特异性。(4)结论：我们的团队已经创建了一个经过技术和临床验证的儿科数字AI听诊平台。使用我们的平台可以提高儿科患者临床护理的效果和效率，减少父母的焦虑，并节省成本。
(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid—a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics—that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still’s murmur identification and (2) wheeze detection. The platform has been deployed in four children’s medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still’s murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.
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影响因子：
2.6
作者：
Shekhar, Raj;Vanama, Ganesh;John, Titus;Issac, James;Arjoune, Youness;Doroshow, Robin W.
通讯作者：
Doroshow, Robin W.
影响因子：
4.6
作者：
Kim, Beom Joon;Kim, Baek Seung;Mun, Jeong Hyeon;Lim, Changwon;Kim, Kyung Hoon
通讯作者：
Kim, Kyung Hoon
影响因子：
--
作者：
Arjoune, Youness;Nguyen, Trong N. N.;Shekhar, Raj
通讯作者：
Shekhar, Raj
影响因子：
7.7
作者：
Bokov, Plamen;Mahut, Bruno;Delclaux, Christophe
通讯作者：
Delclaux, Christophe
DOI：
10.3122/jabfm.2015.03.140248
发表时间：
2015-05-01
影响因子：
2.9
作者：
Kuhn, Lindsay;Reeves, Kelly;Dulin, Michael
通讯作者：
Dulin, Michael