Machine learning in radiology: the new frontier in interstitial lung diseases

H Barnes, SM Humphries, PM George… - The Lancet Digital …, 2023 - thelancet.com
Challenges for the effective management of interstitial lung diseases (ILDs) include
difficulties with the early detection of disease, accurate prognostication with baseline data …

Beyond visual interpretation: quantitative analysis and artificial intelligence in interstitial lung disease diagnosis “expanding horizons in radiology”

G Rea, N Sverzellati, M Bocchino, R Lieto, G Milanese… - Diagnostics, 2023 - mdpi.com
Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological
conditions affecting the lung parenchyma and interstitial network. There are approximately …

ADU-Net: an attention dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images

S Saha, S Dutta, B Goswami, D Nandi - Biomedical Signal Processing and …, 2023 - Elsevier
An automatic method for qualitative and quantitative evaluation of chest Computed
Tomography (CT) images is essential for diagnosing COVID-19 patients. We aim to develop …

Recent advances of artificial intelligence applications in interstitial lung diseases

KP Exarchos, G Gkrepi, K Kostikas, A Gogali - Diagnostics, 2023 - mdpi.com
Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying
in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even …

Segmentation and classification of interstitial lung diseases based on hybrid deep learning network model

SR Vinta, B Lakshmi, MA Safali, GSC Kumar - IEEE Access, 2024 - ieeexplore.ieee.org
Interstitial lung diseases (ILD) are diverse diseases that share pathological, radiological,
and clinical traits and involve interstitial fibrosis and inflammation. These have a significant …

The potential role of artificial intelligence in the clinical practice of interstitial lung disease

T Handa - Respiratory Investigation, 2023 - Elsevier
Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as
drug discovery, diagnostic support, and assistance with medical practice. Among these …

Deep learning for the early identification of periodontitis: a retrospective, multicentre study

Q Liu, F Dai, H Zhu, H Yang, Y Huang, L Jiang, X Tang… - Clinical Radiology, 2023 - Elsevier
Aim To develop a deep-learning model to help general dental practitioners diagnose
periodontitis accurately and at an early stage. Materials and methods First, the panoramic …

CNN architecture for lung cancer detection

C Tejaswini, P Nagabushanam… - 2022 IEEE 11th …, 2022 - ieeexplore.ieee.org
Lungs play major role for the respiratory rate in a human being. Lung cancer can be
detected only after the disease spreads to neighboring parts and hence detecting lung …

Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT

X Huang, W Si, X Ye, Y Zhao, H Gu… - BMJ Open …, 2024 - bmjopenrespres.bmj.com
Purpose Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is the primary cause of
death in patients with IPF, characterised by diffuse, bilateral ground-glass opacification on …

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia

P Nagpal, J Guo, KM Shin, JK Lim, KB Kim… - BJR| Open, 2021 - academic.oup.com
Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing
novel insights into chronic inflammatory lung diseases, including chronic obstructive …