Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

McDowell, Andrea; Kang, Juwon; Yang, Jinho; Jung, Jihee; Oh, Yeon‐Mok; Kym, Sungmin; Shin, Tae-Seop; Kim, Tae‐Bum; Jee, Young Koo; Kim, Eui Chong

doi:10.1038/s12276-022-00846-5

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…While CT can provide detailed structural information with its high-resolution 3D imaging capabilities, other data, such as PFT, 39 clinical data, 40,41 chest x-ray, 42 and magnetic resonance imaging (MRI), 43 can also provide useful information. PFT is noninvasive, and allows for longitudinal tracking of lung function, but is limited to characterizing anatomical changes.…”

Section: Single-modality Versus Multimodalitymentioning

confidence: 99%

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

Wang,

Li,

Gao

et al. 2024

MedComm – Future Medicine

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Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT‐based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI's impact on COPD management.

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Section: Single-modality Versus Multimodalitymentioning

confidence: 99%

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

Wang,

Li,

Gao

et al. 2024

MedComm – Future Medicine

View full text Add to dashboard Cite

show abstract

“…Benefiting from the rapid advancement of medical technology, many studies have been able to use OTU data for analysis [7,20]. Pasolli et al [22] comprehensively evaluated the prediction tasks based on shotgun metagenomics and the method of microbial phenotype association evaluation, mainly using support vector machines and random forest models to predict diseases, and with the help of Lasso and elastic net (ENet) Regularized Multiple Logistic Regression.…”

Section: Related Workmentioning

confidence: 99%

Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment

Chen

Liu

et al. 2023

World Wide Web

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Entity alignment is an essential process in knowledge graph (KG) fusion, which aims to link entities representing the same real-world object in different KGs, to achieve entity expansion and graph fusion. Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.

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“…AI techniques range in complexity from machine learning (ML) algorithms to deep learning (DL). While simple models have been used since the 1960s in the form of logistic regression, recent decades have seen the development of sophisticated neural network algorithms to predict risk for breast, lung, and other cancers [20][21][22][23][24][25][26][27]. The area under the curve (AUC) is a commonly used metric in risk modeling.…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Jiang

Chao

Culp

et al. 2023

Cancers

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Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65–75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

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Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

Cited by 9 publications

References 47 publications

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

Computed tomography‐based artificial intelligence in lung disease—Chronic obstructive pulmonary disease

Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

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