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J. Mol. Sci.","IJMS"],"abstract":"<jats:p>Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease imposing a substantial health and economic burden. Artificial intelligence (AI) has emerged as a promising tool to address CRS complexity in both clinical and research settings. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, and Embase for peer-reviewed articles utilizing machine learning, deep learning, and natural language processing, with a focus on molecular-level or immunologic mechanisms in CRS. Thirty-one studies were included in the qualitative synthesis. Our results demonstrate that while AI-driven analysis helps stratify patient endotypes, the identified schemes exhibit substantial heterogeneity in cluster numbers and features, raising concerns about their reproducibility. AI applications also included histopathological image interpretation, drug discovery, biologic therapy selection, and prognostic modeling to evaluate revision surgery risks and olfactory dysfunction. However, using the PROBAST+AI framework, we found that 87.1% of the assessed studies carry a high overall risk of bias, mainly due to the reliance on apparent performance or internal validation and limited independent evaluation of model performance, indicating that many reported performance estimates may be optimistic. Future large-scale, multicenter collaborations integrating AI into clinical workflows will be essential to establish reliable and generalizable AI applications for personalized CRS management.<\/jats:p>","DOI":"10.3390\/ijms27198812","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T09:10:48Z","timestamp":1790932248000},"page":"8812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Chronic Rhinosinusitis: From Molecular Profiling to Personalized Clinical Management\u2014A Systematic Review"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4217-2455","authenticated-orcid":false,"given":"Chi-Sheng","family":"Huang","sequence":"first","affiliation":[{"name":"School of Medicine, National Taiwan University, Taipei 100233, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duen-Lii","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Head and Neck Surgery, National Taiwan University Hospital, Taipei 100225, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin-Yu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701301, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maysayawan","family":"Sreesawat","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3818-2818","authenticated-orcid":false,"given":"Te-Huei","family":"Yeh","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Head and Neck Surgery, National Taiwan University Hospital, Taipei 100225, Taiwan"},{"name":"Department of Otolaryngology Head and Neck Surgery, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu 300195, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4409-9474","authenticated-orcid":false,"given":"Chih-Feng","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Head and Neck Surgery, National Taiwan University Hospital, Taipei 100225, Taiwan"},{"name":"Institute of Medical Device and Imaging, National Taiwan University, Taipei 100233, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T., Alshaya, A.I., Almohareb, S.N., Aldairem, A., Alrashed, M., Bin Saleh, K., and Badreldin, H.A. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med. Educ., 23.","DOI":"10.1186\/s12909-023-04698-z"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, Y.Y., Jiang, S.P., and Wang, Y.B. (2025). Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis. Front. Physiol., 16.","DOI":"10.3389\/fphys.2025.1522090"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/0010-4809(75)90009-9","article-title":"Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN system","volume":"8","author":"Shortliffe","year":"1975","journal-title":"Comput. Biomed. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1038\/s41568-024-00694-7","article-title":"A guide to artificial intelligence for cancer researchers","volume":"24","author":"Mahmood","year":"2024","journal-title":"Nat. Rev. Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100228","DOI":"10.1016\/j.xops.2022.100228","article-title":"Artificial Intelligence Detection of Diabetic Retinopathy: Subgroup Comparison of the EyeArt System with Ophthalmologists\u2019 Dilated Examinations","volume":"3","author":"Lim","year":"2023","journal-title":"Ophthalmol. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1161\/CIRCULATIONAHA.120.047829","article-title":"Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke","volume":"143","author":"Raghunath","year":"2021","journal-title":"Circulation"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Becker, J., Decker, J.A., R\u00f6mmele, C., Kahn, M., Messmann, H., Wehler, M., Schwarz, F., Kroencke, T., and Scheurig-Muenkler, C. (2022). Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs. Diagnostics, 12.","DOI":"10.3390\/diagnostics12061465"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"16444","DOI":"10.1038\/s41598-018-34753-5","article-title":"Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy","volume":"8","author":"Huang","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1186\/s12967-020-02658-5","article-title":"Precision medicine in the era of artificial intelligence: Implications in chronic disease management","volume":"18","author":"Subramanian","year":"2020","journal-title":"J. Transl. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Han, K., Cao, P., Wang, Y., Xie, F., Ma, J., Yu, M., Wang, J., Xu, Y., Zhang, Y., and Wan, J. (2021). A Review of Approaches for Predicting Drug-Drug Interactions Based on Machine Learning. Front. Pharmacol., 12.","DOI":"10.3389\/fphar.2021.814858"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1097\/FTD.0000000000001078","article-title":"Artificial Intelligence and Machine Learning Approaches to Facilitate Therapeutic Drug Management and Model-Informed Precision Dosing","volume":"45","author":"Poweleit","year":"2023","journal-title":"Ther. Drug Monit."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chowdhary, K.R. (2020). Fundamentals of Artificial Intelligence, Springer.","DOI":"10.1007\/978-81-322-3972-7"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1007\/s11596-021-2474-3","article-title":"Application of Artificial Intelligence in Medicine: An Overview","volume":"41","author":"Liu","year":"2021","journal-title":"Curr. Med. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rahmani, A.M., Yousefpoor, E., Yousefpoor, M.S., Mehmood, Z., Haider, A., Hosseinzadeh, M., and Ali Naqvi, R. (2021). Machine Learning (ML) in Medicine: Review, Applications, and Challenges. Mathematics, 9.","DOI":"10.3390\/math9222970"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mohammed, M., Khan, M., and Bashier, E. (2016). Machine Learning: Algorithms and Applications, CRC Press.","DOI":"10.1201\/9781315371658"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1049\/cit2.12180","article-title":"Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey","volume":"8","author":"Gheisari","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8261","DOI":"10.1007\/s13369-020-05212-z","article-title":"Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm","volume":"46","author":"Hamed","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106628","DOI":"10.1016\/j.cmpb.2022.106628","article-title":"Semi-supervised learning for medical image classification using imbalanced training data","volume":"216","author":"Huynh","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5472","DOI":"10.1002\/mp.13237","article-title":"Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study","volume":"45","author":"Gong","year":"2018","journal-title":"Med. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1016\/j.ekir.2020.05.007","article-title":"Hierarchical Clustering Analysis for Predicting 1-Year Mortality After Starting Hemodialysis","volume":"5","author":"Komaru","year":"2020","journal-title":"Kidney Int. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107280","DOI":"10.1016\/j.cmpb.2022.107280","article-title":"Reinforcement learning strategies in cancer chemotherapy treatments: A review","volume":"229","author":"Yang","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1007\/s10479-020-03871-7","article-title":"Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec","volume":"312","author":"Khalilpourazari","year":"2022","journal-title":"Ann. Oper. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9810","DOI":"10.1038\/s41598-022-13750-9","article-title":"Improved diagnosis of rheumatoid arthritis using an artificial neural network","volume":"12","author":"Bai","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106649","DOI":"10.1016\/j.compbiomed.2023.106649","article-title":"Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review","volume":"155","author":"Hossain","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Meystre, S.M., Heider, P.M., Cates, A., Bastian, G., Pittman, T., Gentilin, S., and Kelechi, T.J. (2023). Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models. BMC Med. Res. Methodol., 23.","DOI":"10.1186\/s12874-023-01916-6"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4193\/Rhin20.401","article-title":"European Position Paper on Rhinosinusitis and Nasal Polyps 2020","volume":"58","author":"Fokkens","year":"2020","journal-title":"Rhinology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1016\/j.jaip.2022.01.016","article-title":"Epidemiology of Chronic Rhinosinusitis: Prevalence and Risk Factors","volume":"10","author":"Sedaghat","year":"2022","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1177\/194589240301700106","article-title":"The economic burden and symptom manifestations of chronic rhinosinusitis","volume":"17","author":"Bhattacharyya","year":"2003","journal-title":"AM. J. Rhinol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1177\/000348941112000701","article-title":"Incremental health care utilization and expenditures for chronic rhinosinusitis in the United States","volume":"120","author":"Bhattacharyya","year":"2011","journal-title":"Ann. Otol. Rhinol. Laryngol."},{"key":"ref_30","first-page":"213","article-title":"Direct and indirect costs of adult patients with chronic rhinosinusitis with nasal polyps","volume":"58","author":"Lourijsen","year":"2020","journal-title":"Rhinology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1016\/j.jaci.2013.02.036","article-title":"Endotypes and phenotypes of chronic rhinosinusitis: A PRACTALL document of the European Academy of Allergy and Clinical Immunology and the American Academy of Allergy, Asthma & Immunology","volume":"131","author":"Akdis","year":"2013","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.jaip.2016.05.004","article-title":"Phenotypes and Emerging Endotypes of Chronic Rhinosinusitis","volume":"4","author":"Bachert","year":"2016","journal-title":"J. Allergy Clin. Immunol. Pr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1111\/all.15074","article-title":"Endotypes of chronic rhinosinusitis: Relationships to disease phenotypes, pathogenesis, clinical findings, and treatment approaches","volume":"77","author":"Kato","year":"2022","journal-title":"Allergy"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tai, J., Han, M., and Kim, T.H. (2022). Therapeutic Strategies of Biologics in Chronic Rhinosinusitis: Current Options and Future Targets. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23105523"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1001\/jamaoto.2020.1453","article-title":"Contemporary Classification of Chronic Rhinosinusitis Beyond Polyps vs No Polyps: A Review","volume":"146","author":"Grayson","year":"2020","journal-title":"JAMA Otolaryngol. Head Neck Surg."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3285","DOI":"10.1016\/j.jaip.2023.07.043","article-title":"Artificial intelligence\u2019s potential in tailoring prescription of biologic therapy for chronic rhinosinusitis","volume":"11","author":"Minzoni","year":"2023","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","year":"2025","journal-title":"BMJ"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"l6890","DOI":"10.1136\/bmj.l6890","article-title":"Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline","volume":"368","author":"Campbell","year":"2020","journal-title":"BMJ"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s00405-016-4315-8","article-title":"Using preoperative unsupervised cluster analysis of chronic rhinosinusitis to inform patient decision and endoscopic sinus surgery outcome","volume":"274","author":"Adnane","year":"2017","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"ref_41","first-page":"1","article-title":"Prediction of phenotypes by secretory biomarkers and machine learning in patients with chronic rhinosinusitis","volume":"29","author":"Becker","year":"2025","journal-title":"Eur. Rev. Med. Pharmacol. Sci."},{"key":"ref_42","first-page":"17","article-title":"Construction and analysis of a ceRNA network and patterns of immune infiltration in chronic rhinosinusitis with nasal polyps: Based on data mining and experimental verification","volume":"12","author":"Chen","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, Z., Qu, L., Hao, Q., Teng, S., Liu, S., Wu, Q., Yi, H., Shen, X., Li, L., and Xu, Z. (2025). Identification of anoikis-related genes classification patterns and immune infiltration characterization in chronic rhinosinusitis with nasal polyps based on machine learning. Front. Mol. Biosci., 12.","DOI":"10.3389\/fmolb.2025.1624300"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1002\/alr.22449","article-title":"Baseline mucus cytokines predict 22-item Sino-Nasal Outcome Test results after endoscopic sinus surgery","volume":"10","author":"Chowdhury","year":"2020","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1111\/cea.12463","article-title":"Immunological profiling in chronic rhinosinusitis with nasal polyps reveals distinct VEGF and GM-CSF signatures during symptomatic exacerbations","volume":"45","author":"Divekar","year":"2015","journal-title":"Clin. Exp. Allergy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1002\/alr.21904","article-title":"Unsupervised network mapping of commercially available immunoassay yields three distinct chronic rhinosinusitis endotypes","volume":"7","author":"Divekar","year":"2017","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jaci.2025.03.029","article-title":"Effects of inflammatory endotypes on disease trajectory in chronic rhinosinusitis with nasal polyps","volume":"156","author":"Dorismond","year":"2025","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_48","first-page":"15","article-title":"Predicting difficult-to-treat chronic rhinosinusitis by noninvasive biological markers","volume":"59","author":"Guo","year":"2021","journal-title":"Rhinology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1002\/alr.23365","article-title":"Multiple instance learning for eosinophil quantification of sinonasal histopathology images: A hierarchical determination on whole slide images","volume":"14","author":"Hsu","year":"2024","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ishino, T., Oda, T., Kawasumi, T., Takemoto, K., Nishida, M., Horibe, Y., Chikuie, N., Taruya, T., Hamamoto, T., and Ueda, T. (2024). Severe Type 2 Inflammation Leads to High Platelet-Activating-Factor-Associated Pathology in Chronic Rhinosinusitis with Nasal Polyps-A Hierarchical Cluster Analysis Using Bulk RNA Barcoding and Sequencing. Int. J. Mol. Sci., 25.","DOI":"10.20944\/preprints202401.1320.v1"},{"key":"ref_51","first-page":"173","article-title":"Subepithelial neutrophil infiltration as a predictor of the surgical outcome of chronic rhinosinusitis with nasal polyps","volume":"59","author":"Kim","year":"2021","journal-title":"Rhinology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.anai.2024.05.023","article-title":"Factors for predicting the outcome of surgery for non-eosinophilic chronic rhinosinusitis with nasal polyps","volume":"133","author":"Li","year":"2024","journal-title":"Ann. Allergy Asthma Immunol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1111\/all.13411","article-title":"Multidimensional endotypes of chronic rhinosinusitis and their association with treatment outcomes","volume":"73","author":"Liao","year":"2018","journal-title":"Allergy"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.anai.2022.02.025","article-title":"Microvessel quantification by fully convolutional neural networks associated with type 2 inflammation in chronic rhinosinusitis","volume":"128","author":"Liu","year":"2022","journal-title":"Ann. Allergy Asthma Immunol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"150","DOI":"10.4193\/Rhino15.271","article-title":"Cellular phenotyping of chronic rhinosinusitis with nasal polyps","volume":"54","author":"Lou","year":"2016","journal-title":"Rhinology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1002\/alr.22380","article-title":"Discriminant analysis followed by unsupervised cluster analysis including exosomal cystatins predict presence of chronic rhinosinusitis, phenotype, and disease severity","volume":"9","author":"Miyake","year":"2019","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/alr.22249","article-title":"Patterns of olfactory dysfunction in chronic rhinosinusitis identified by hierarchical cluster analysis and machine learning algorithms","volume":"9","author":"Morse","year":"2019","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1016\/j.jaci.2021.11.017","article-title":"Inflammatory molecular endotypes of nasal polyps derived from White and Japanese populations","volume":"149","author":"Nakayama","year":"2022","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1002\/ohn.904","article-title":"Inflammatory Profile of Chronic Rhinosinusitis With Nasal Polyp Patients in Brazil: Multicenter Study","volume":"171","author":"Romano","year":"2024","journal-title":"Otolaryngol. Head Neck Surg."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Sireci, F., Lorusso, F., Immordino, A., Centineo, M., Gerardi, I., Patti, G., Rusignuolo, S., Manzella, R., Gallina, S., and Dispenza, F. (2024). ChatGPT as a New Tool to Select a Biological for Chronic Rhino Sinusitis with Polyps, \u201cCaution Advised\u201d or \u201cDistant Reality\u201d?. J. Pers. Med., 14.","DOI":"10.3390\/jpm14060563"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1016\/j.jaci.2021.01.021","article-title":"Endotyping chronic rhinosinusitis based on olfactory cleft mucus biomarkers","volume":"147","author":"Soler","year":"2021","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1002\/alr.22632","article-title":"Machine learning of biomarkers and clinical observation to predict eosinophilic chronic rhinosinusitis: A pilot study","volume":"11","author":"Thorwarth","year":"2021","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1016\/j.jaci.2015.12.1324","article-title":"Inflammatory endotypes of chronic rhinosinusitis based on cluster analysis of biomarkers","volume":"137","author":"Tomassen","year":"2016","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1016\/j.jaci.2018.02.002","article-title":"Identification of clinically relevant chronic rhinosinusitis endotypes using cluster analysis of mucus cytokines","volume":"141","author":"Turner","year":"2018","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Viksne, R., Sumeraga, G., and Pilmane, M. (2024). Endotypes of Chronic Rhinosinusitis with Primary and Recurring Nasal Polyps in the Latvian Population. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25105159"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"7573","DOI":"10.2147\/JIR.S484914","article-title":"Identification of Potential Feature Genes in CRSwNP Using Bioinformatics Analysis and Machine Learning Strategies","volume":"17","author":"Wang","year":"2024","journal-title":"J. Inflamm. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.jaci.2022.10.010","article-title":"Endotypes of chronic rhinosinusitis based on inflammatory and remodeling factors","volume":"151","author":"Wang","year":"2023","journal-title":"J. Allergy Clin. Immunol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Q., Duan, S., Zhang, Y., Zhao, L., Zhang, S., Hao, L., Li, Y., Wang, X., and Wang, C. (2022). A diagnostic model for predicting type 2 nasal polyps using biomarkers in nasal secretion. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1054201"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"103336","DOI":"10.1016\/j.ebiom.2021.103336","article-title":"Artificial intelligence for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging","volume":"66","author":"Wu","year":"2021","journal-title":"eBioMedicine"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhou, J., Wang, H., Wang, J., and Zhou, F. (2025). Discovering biomarkers for chronic sinusitis with nasal polyps: A study integrating bioinformatics analysis and experimental validation of macrophage polarization and metabolism-related genes. Front. Bioinform., 5.","DOI":"10.3389\/fbinf.2025.1613136"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1016\/j.jaip.2020.03.007","article-title":"Endotypes of Chronic Rhinosinusitis with Nasal Polyps: Pathology and Possible Therapeutic Implications","volume":"8","author":"Bachert","year":"2020","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1002\/alr.23056","article-title":"Site-specific detection and differential levels of immune mediators in the sinonasal mucosa","volume":"13","author":"Lopez","year":"2023","journal-title":"Int. Forum Allergy Rhinol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"396","DOI":"10.2500\/ajra.2013.27.3916","article-title":"Histopathological evaluation of chronic rhinosinusitis: A critical review","volume":"27","author":"Jiang","year":"2013","journal-title":"Am. J. Rhinol. Allergy"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.drudis.2020.10.010","article-title":"Artificial intelligence in drug discovery and development","volume":"26","author":"Paul","year":"2021","journal-title":"Drug Discov. Today"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1093\/bib\/bbac267","article-title":"Machine-designed biotherapeutics: Opportunities, feasibility and advantages of deep learning in computational antibody discovery","volume":"23","author":"Wilman","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Manzella, R., Immordino, A., Galletti, C., Giammona Indaco, F., Stilo, G., Messina, G., Lorusso, F., Gargano, R., Frangipane, S., and Giunta, G. (2025). ChatGPT in the Management of Chronic Rhinosinusitis with Nasal Polyps: Promising Support or Digital Illusion? Insights from a Multicenter Observational Study. J. Clin. Med., 14.","DOI":"10.3390\/jcm14134501"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"243","DOI":"10.5694\/j.1326-5377.1991.tb142231.x","article-title":"Functional endoscopic sinus surgery","volume":"155","author":"Bear","year":"1991","journal-title":"Med. J. Aust."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1016\/j.jaip.2023.05.042","article-title":"Artificial Intelligence Chatbots in Allergy and Immunology Practice: Where Have We Been and Where Are We Going?","volume":"11","author":"Goktas","year":"2023","journal-title":"J. Allergy Clin. Immunol. Pract."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J. R. Soc. 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