Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases

Exarchos, Konstantinos P.; Gkrepi, Georgia; Κostikas, Κonstantinos; Gogali, Athena

doi:10.3390/diagnostics13132303

Cited by 14 publications

(7 citation statements)

References 39 publications

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“…Common approaches for predicting ILD involve the use of artificial intelligence (AI). 48 , 49 However, although these approaches can achieve high prediction scores, there is limited information on why certain risk factors may lead to an increased likelihood of ILD. This unfortunately further confounds understanding of the mechanism behind ILD.…”

Section: Discussionmentioning

confidence: 99%

In vitro PK/PD modeling of tyrosine kinase inhibitors in non‐small cell lung cancer cell lines

Wanika,

Evans,

Johnson

et al. 2024

Clinical Translational Sci

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Tyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non‐small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited in vivo studies which focus on TKIs and their effects on different regulation pathways. Many in vitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an in vitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an in vitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include in vitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI‐induced adverse events.

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Section: Discussionmentioning

confidence: 99%

In vitro PK/PD modeling of tyrosine kinase inhibitors in non‐small cell lung cancer cell lines

Wanika,

Evans,

Johnson

et al. 2024

Clinical Translational Sci

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“…However, in the immediate future, these possibilities will allow for an exceptional approach beyond "visual power", providing additional potential markers, most of which are not yet known but can further stratify the patient pattern with ILDs, allowing for the analysis of specific "features", such as radiomics. Today, it is not yet possible to give a specific meaning to these [30][31][32][33][34][35][36][37][38][39].…”

Section: Beyond the Surface: Artificial Intelligence's (Ai) Role In U...mentioning

confidence: 99%

The Unveiled Triad: Clinical, Radiological and Pathological Insights into Hypersensitivity Pneumonitis

Rea,

Bocchino,

Lieto

et al. 2024

JCM

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Hypersensitivity pneumonitis (HP) is a diffuse parenchymal lung disease (DLPD) characterized by complex interstitial lung damage with polymorphic and protean inflammatory aspects affecting lung tissue targets including small airways, the interstitium, alveolar compartments and vascular structures. HP shares clinical and often radiological features with other lung diseases in acute or chronic forms. In its natural temporal evolution, if specific therapy is not initiated promptly, HP leads to progressive fibrotic damage with reduced lung volumes and impaired gas exchange. The prevalence of HP varies considerably worldwide, influenced by factors like imprecise disease classification, diagnostic method limitations for obtaining a confident diagnosis, diagnostic limitations in the correct processing of high-resolution computed tomography (HRCT) radiological parameters, unreliable medical history, diverse geographical conditions, heterogeneous agricultural and industrial practices and occasionally ineffective individual protections regarding occupational exposures and host risk factors. The aim of this review is to present an accurate and detailed 360-degree analysis of HP considering HRCT patterns and the role of the broncho-alveolar lavage (BAL), without neglecting biopsy and anatomopathological aspects and future technological developments that could make the diagnosis of this disease less challenging.

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“…AI tools have proven instrumental in analyzing intricate datasets about ILDs, facilitating accurate diagnosis, classification, and prognosis prediction. The integration of AI in ILDs represents a significant stride toward improving patient outcomes and streamlining clinical workflows in pulmonary medicine [ 45 ].…”

Section: Reviewmentioning

confidence: 99%

“…Recent progress underscores the utilization of AI in ILDs, focusing on screening, diagnosis, and prognosis. Key data repositories such as the Open Source Imaging Consortium (OSIC) data repository, ILD Database from medGIFT, ILDgenDB, and ILDGDB play a pivotal role in facilitating research and development efforts aimed at predicting lung function decline and unraveling gene mechanisms associated with ILDs [ 45 ]. A noteworthy development is the creation of AI tools like Sybil, which are engineered to detect early signs of lung cancer that may elude detection by the naked eye on CT scans.…”

Section: Reviewmentioning

confidence: 99%

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Sindhu,

Jadhav,

Ghewade

et al. 2024

Cureus

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Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

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Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases

Cited by 14 publications

References 39 publications

In vitro PK/PD modeling of tyrosine kinase inhibitors in non‐small cell lung cancer cell lines

In vitro PK/PD modeling of tyrosine kinase inhibitors in non‐small cell lung cancer cell lines

The Unveiled Triad: Clinical, Radiological and Pathological Insights into Hypersensitivity Pneumonitis

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

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