Clinical utilization of artificial intelligence in predicting therapeutic efficacy in pulmonary tuberculosis
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved..
Traditional methods for monitoring pulmonary tuberculosis (PTB) treatment efficacy lack sensitivity, prompting the exploration of artificial intelligence (AI) to enhance monitoring. This review investigates the application of AI in monitoring anti-tuberculosis (ATTB) treatment, revealing its potential in predicting treatment duration, adverse reactions, outcomes, and drug resistance. It provides important insights into the potential of AI technology to enhance monitoring and management of ATTB treatment. Systematic search across six databases from 2013 to 2023 explored AI in forecasting PTB treatment efficacy. Support vector machine and convolutional neural network excel in treatment duration prediction, while random forest, artificial neural network, and classification and regression tree show promise in forecasting adverse reactions and outcomes. Neural networks and random forest are effective in predicting drug resistance. AI advancements offer improved monitoring strategies, better patient prognosis, and pave the way for future AI research in PTB treatment monitoring.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:17 |
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| Enthalten in: |
Journal of infection and public health - 17(2024), 4 vom: 30. März, Seite 632-641 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Fuzhen [VerfasserIn] |
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| Links: |
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| Themen: |
Anti-tuberculosis treatment |
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| Anmerkungen: |
Date Completed 25.03.2024 Date Revised 25.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jiph.2024.02.012 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369181549 |
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| 520 | |a Traditional methods for monitoring pulmonary tuberculosis (PTB) treatment efficacy lack sensitivity, prompting the exploration of artificial intelligence (AI) to enhance monitoring. This review investigates the application of AI in monitoring anti-tuberculosis (ATTB) treatment, revealing its potential in predicting treatment duration, adverse reactions, outcomes, and drug resistance. It provides important insights into the potential of AI technology to enhance monitoring and management of ATTB treatment. Systematic search across six databases from 2013 to 2023 explored AI in forecasting PTB treatment efficacy. Support vector machine and convolutional neural network excel in treatment duration prediction, while random forest, artificial neural network, and classification and regression tree show promise in forecasting adverse reactions and outcomes. Neural networks and random forest are effective in predicting drug resistance. AI advancements offer improved monitoring strategies, better patient prognosis, and pave the way for future AI research in PTB treatment monitoring | ||
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