Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Koul, Apeksha; Bawa, Rajesh K.; Kumar, Yogesh

doi:10.1007/s11831-022-09818-4

Cited by 41 publications

(6 citation statements)

References 148 publications

(112 reference statements)

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“…This study aimed to develop a convenient tool for identifying contagious PTB cases, to aid in the prevention and management of tuberculosis. Deep learning methods have recently shown great promise in disease diagnosis and prediction (23)(24)(25)(26). Several CNNbased deep learning models have been proposed for TB analysis (17,(27)(28)(29)(30)(31)(32)(33)(34)(35).…”

Section: Discussionmentioning

confidence: 99%

Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning

Gao,

Zhang,

et al. 2023

Front. Public Health

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IntroductionThis study aimed to develop and assess a deep-learning model based on CT images for distinguishing infectivity in patients with pulmonary tuberculosis (PTB).MethodsWe labeled all 925 patients from four centers with weak and strong infectivity based on multiple sputum smears within a month for our deep-learning model named TBINet's training. We compared TBINet's performance in identifying infectious patients to that of the conventional 3D ResNet model. For model explainability, we used gradient-weighted class activation mapping (Grad-CAM) technology to identify the site of lesion activation in the CT images.ResultsThe TBINet model demonstrated superior performance with an area under the curve (AUC) of 0.819 and 0.753 on the validation and external test sets, respectively, compared to existing deep learning methods. Furthermore, using Grad-CAM, we observed that CT images with higher levels of consolidation, voids, upper lobe involvement, and enlarged lymph nodes were more likely to come from patients with highly infectious forms of PTB.ConclusionOur study proves the feasibility of using CT images to identify the infectivity of PTB patients based on the deep learning method.

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Section: Discussionmentioning

confidence: 99%

Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning

Gao,

Zhang,

et al. 2023

Front. Public Health

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“…Trained AI models with knowledge of electronic health records and radiological imaging can significantly improve the efficiency and accuracy of the diagnosis and management of lung diseases such as COPD and pneumoconiosis. Following are the articles that showcased the application of AI in the management of the two most important lung diseases: COPD and pneumoconiosis [1][2][3][4][5][6][7].…”

Section: Ai Techniques and Tools Usedmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

Hussain,

Marlowe,

Ali

et al. 2024

Cureus

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This systematic review examines the transformative impact of artificial intelligence (AI) in managing lung disorders through a comprehensive analysis of articles spanning 2014 to 2023. Evaluating AI's multifaceted roles in radiological imaging, disease burden prediction, detection, diagnosis, and molecular mechanisms, this review presents a critical synthesis of key insights from select articles. The findings underscore AI's significant strides in bolstering diagnostic accuracy, interpreting radiological imaging, predicting disease burdens, and deepening the understanding of tuberculosis (TB), chronic obstructive pulmonary disease (COPD), silicosis, pneumoconiosis, and lung fibrosis. The synthesis positions AI as a revolutionary tool within the healthcare system, offering vital implications for healthcare workers, policymakers, and researchers in comprehending and leveraging AI's pivotal role in lung disease management.

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“…Hence, we propose the following statement: RP2: We suggest further investigation on how AI in diagnostics might be applied in different clinical settings and validated using bigger datasets with improved validity. In addition, we recommend testing AI for illness diagnosis in a real-world setting to determine its applicability [70,83].…”

Section: Corroboration and Portabilitymentioning

confidence: 99%

Integrating Artificial Intelligence in Disease Diagnosis, Treatment, and Formulation Development: A Review

Kumar,

Ahmed

et al. 2023

Asian J Pharm Clin Res

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Artificial intelligence (AI) is rapidly advancing and significantly impacting clinical care and treatment. Machine learning and deep learning, as core digital AI technologies, are being extensively applied to support diagnosis and treatment. With the progress of digital health-care technologies such as AI, bioprinting, robotics, and nanotechnology, the health-care landscape is transforming. Digitization in health-care offers various opportunities, including reducing human error rates, improving clinical outcomes, and monitoring longitudinal data. AI techniques, ranging from learning algorithms to deep learning, play a critical role in several health-care domains, such as the development of new health-care systems, improvement of patient information and records, and treatment of various ailments. AI has emerged as a powerful scientific tool, capable of processing and analyzing vast amounts of data to support decision-making. Numerous studies have demonstrated that AI can perform on par with or outperform humans in crucial medical tasks, including disease detection. However, despite its potential to revolutionize health care, ethical considerations must be carefully addressed before implementing AI systems and making informed decisions about their usage. Researchers have utilized various AI-based approaches, including deep and machine learning models, to identify diseases that require early diagnosis, such as skin, liver, heart, and Alzheimer’s diseases. Consequently, related work presents different methods for disease diagnosis along with their respective levels of accuracy, including the Boltzmann machine, K nearest neighbor, support vector machine, decision tree, logistic regression, fuzzy logic, and artificial neural network. While AI holds immense promise, it is likely to take decades before it completely replaces humans in various medical operations.

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Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Cited by 41 publications

References 148 publications

Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning

Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

Integrating Artificial Intelligence in Disease Diagnosis, Treatment, and Formulation Development: A Review

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