Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB.
Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB.
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DOI：
10.1038/s41598-023-31772-9
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发表时间：
2023-03-23
影响因子：
4.6
通讯作者：
Pamarthi, Kiran
中科院分区：
文献类型：
--
作者：
Yellapu, Gayatri Devi;Rudraraju, Gowrisree;Sripada, Narayana Rao;Mamidgi, Baswaraj;Jalukuru, Charan;Firmal, Priyanka;Yechuri, Venkat;Varanasi, Sowmya;Peddireddi, Venkata Sudhakar;Bhimarasetty, Devi Madhavi;Kanisetti, Sidharth;Joshi, Niranjan;Mohapatra, Prasant;Pamarthi, Kiran
声信号分析已被应用于各种医疗设备中。然而，涉及咳嗽音分析以筛查潜在的肺结核(PTB)嫌疑人的研究很少。这项横断面验证研究的主要目标是开发和验证斯瓦萨人工智能平台，以根据受试者提供的标志性咳嗽声和症状信息来筛查和优先处理高危患者的肺结核。自愿的咳嗽声数据是在印度安得拉医学院收集的。建立了基于多模式卷积神经网络结构和前馈人工神经网络(表格特征)的算法，并对278例阳性和289例阴性的567例肺结核患者进行了验证。这两个模型的输出结合在一起，以检测可能存在的肺结核(阳性病例)。在临床验证阶段，AI-模型对检测可能存在的肺结核的准确率为86.82%，敏感性为90.36%，特异性为84.67%。在印度RHC Simhachalam外围卫生保健中心对65例推定肺结核病例进行了模型的试点测试。其中，15名受试者真的是肺结核阳性，阳性预测值为75%。模型的验证结果令人振奋。这一平台有可能满足成本效益高的肺结核筛查方法的未得到满足的需求。它可以远程工作，即时显示结果，而且不需要训练有素的操作员。因此，它可以在世界上各种交通不便、资源匮乏的地区实施。
Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.
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影响因子：
24.3
作者：
Topalovic, Marko;Das, Nilakash;Vanhauwaert, A.
通讯作者：
Vanhauwaert, A.
DOI：
10.1186/1745-9974-2-8
发表时间：
2006-09-28
期刊：
Cough (London, England)
影响因子：
--
作者：
Barry, Samantha J;Dane, Adrie D;Morice, Alyn H;Walmsley, Anthony D
通讯作者：
Walmsley, Anthony D
DOI：
10.1109/ojemb.2020.3026928
发表时间：
2020
影响因子：
5.8
作者：
Laguarta J;Hueto F;Subirana B
通讯作者：
Subirana B
影响因子：
81.5
作者：
Pai, Madhukar;Behr, Marcel A.;Raviglione, Mario
通讯作者：
Raviglione, Mario
影响因子：
3.8
作者：
Swarnkar, Vinayak;Abeyratne, Udantha R.;Triasih, Rina
通讯作者：
Triasih, Rina