Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.

Objective To ascertain the function of necroptosis in IPF (idiopathic pulmonary fibrosis) using bioinformatic techniques. Methods GSE10667 and GSE24206 datasets were obtained from the Gene Expression Omnibus (GEO) database. Necroptosis-related differentially expressed genes (NRDEGs) were identified based on the differentially expressed gene (DEG) and necroptosis gene collection. The gene enrichment signaling pathways in IPF were assessed using gene set enrichment analysis (GSEA). Protein-protein interaction (PPI) networks were created and visualized using the STRING database and Cytoscape, which also identified essential NRDEG functional components. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for pathway enrichment analyses of NRDEGs. The necroptosis-related transcription factor-target gene regulatory network was built using the CellMiner database, and immune infiltration patterns were examined using the CIBERSORTx algorithm. Results: IPF samples showed significant enrichment and activation of the necroptosis pathway. PEL1, MEFV, and SERTAD1 were among the 44 NRDEGs identified.Hub genes were abundant in the IL-17 signaling pathway, RIG-I-like receptor signaling pathway, and apoptosis, and the NRDEGs were largely involved in endopeptidase activity and ficolin-1-rich granules. Twenty-two possible immune cells, including neutrophils, NK cells, CD4, and CD8, were elevated in both datasets. Conclusion We found differential genes related to IPF necroptosis and various immune cell infiltrates, among which CHL1, EGFR, and KRT14, and NRDEG-related drugs and compounds might provide new targets for treatment of IPF.