Enhancing Medical Diagnosis with AI: A Focus on Respiratory Disease Detection.
Background: Artificial intelligence (AI) is revolutionizing medical diagnosis and healthcare, providing constant support to medical practitioners. Intelligent systems alleviate workload pressure while optimizing practitioner performance. AI and deep learning have also improved medical imaging and au...
| Publicado en: | Indian Journal of Community Medicine Vol. 48; no. 5; pp. 709 - 715 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wolters Kluwer India Pvt Ltd
Sep/Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=172215824&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 172215824 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09700218 1CQJ jtl: Indian Journal of Community Medicine issn: 09700218 maglogo: N pubinfo: dt: Sep/Oct2023 vid: 48 iid: 5 pid: 16919 pub: Wolters Kluwer India Pvt Ltd artinfo: ui: 172215824 172215824 172215824 10.4103/ijcm.ijcm_976_22 172215824 ppf: 709 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Enhancing Medical Diagnosis with AI: A Focus on Respiratory Disease Detection. aug: au: Sharma, Sachin Pandey, Siddhant Shah, Dharmesh affil: Department of Big Data Analytics, Adani Institute of Digital Technology Management, Gandhinagar, Gujarat, India sug: subj: Respiratory Tract Diseases Diagnosis Diagnosis, Computer Assisted Artificial Intelligence Noninvasive Procedures Human Audiorecording Stethoscopes Neural Networks (Computer) Respiratory Sounds Descriptive Statistics Sensitivity and Specificity Early Diagnosis Software Workload Job Performance ab: Background: Artificial intelligence (AI) is revolutionizing medical diagnosis and healthcare, providing constant support to medical practitioners. Intelligent systems alleviate workload pressure while optimizing practitioner performance. AI and deep learning have also improved medical imaging and audio analysis. Material and Methods: This research focuses on predicting respiratory diseases using audio recordings from an electronic stethoscope. A convolutional neural network (CNN) was trained on a Respiratory Sound Database, augmented to generate 1,428 audio files. Techniques such as pitch shifting, time stretching, noise addition, time and frequency masking, dynamic range compression, and resampling were employed to increase the diversity and size of the training data. Result: Features were extracted from mono audio files, creating a four layer CNN with 90% accuracy. The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhanci ng their performance. The study highlights AI's potential in respiratory disease detection through audio analysis. Conclusion: The software, developed using the CNN model and Streamlit python library, offers a new tool for early and accurate diagnosis, reducing the burden on medical practitioners and enhancing their performance. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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