Diagnostic applications of artificial intelligence in chronic rhinosinusitis: A systematic review

Antonella Loperfido; Rosa Maria Minniti; Eugenia Maiorano; Carlo Cavaliere; Federico Sireci; Stefano Taboni; Jacopo Zocchi; Pasquale D'Alessio

doi:10.23750/abm.v96i2.16298

Diagnostic applications of artificial intelligence in chronic rhinosinusitis: A systematic review

Authors

Antonella Loperfido Department of Otolaryngology, San Camillo Forlanini Hospital, Rome, Italy https://orcid.org/0000-0002-7174-8126
Rosa Maria Minniti Department of Otolaryngology-Head and Neck Surgery, Azienda USL di Modena, Ospedale Ramazzini di Carpi, Carpi (Modena), Italy
Eugenia Maiorano Department of Otorhinolaryngology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
Carlo Cavaliere Department of Sense Organs, Sapienza University, Rome, Italy
Federico Sireci Otorhinolaryngology Section, Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C), University of Palermo, Palermo, Italy
Stefano Taboni Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Neurosciences, University of Padua-Azienda Ospedale Università di Padova, Padua, Italy
Jacopo Zocchi Department of Otorhinolaryngology and Head and Neck Surgery, IEO, European Institute of Oncology, IRCCS, Milan, Italy
Pasquale D'Alessio Otolaryngology Unit, Ospedale Civile Santo Spirito, Pescara, Italy.

Keywords:

artificial intelligence, chronic rhinosinusitis, diagnostic imaging, deep learning, machine learning, systematic review, rhinology, radiology, computed tomography, paranasal sinuses

Abstract

Background and aim: Artificial Intelligence (AI) in healthcare is rapidly expanding and researchers are exploring its possible role in assisting physicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning. This systematic review aims to summarize the evidence about the role of AI, Machine Learning (ML), and Deep Learning (DL) in the diagnostic imaging of chronic rhinosinusitis (CRS).

Methods: The search strategy was performed according to PRISMA guidelines for systematic reviews. The authors searched all articles in three major medical databases (PubMed, Scopus, Cochrane Library) using the following key terms: “Artificial Intelligence” or “Machine Learning” or “Deep Learning” or “Neural Convolution Learning” or “Knowledge Engineering” and “Nose” or "Nasal” or “Septum” or “Turbinate” or “Sinus” or “Rhinology” or “Sinusitis” or “Rhinosinusitis” or “Chronic Rhinosinusitis” or “Chronic Sinusitis” or “CRS” and “CT” or “MRI” or “Computed Tomography” or “Images” or “CBCT” or “Magnetic Resonance Imaging” or “Imaging” or “Radiographs” or “X-ray”.

Results: Overall, 395 manuscripts were identified, and after duplicate removal (27 articles), excluding off-topic studies (298) and for other structural reasons (50) papers were assessed for eligibility; finally, only 20 papers were included and summarized in the review.

Conclusions: Despite the growing interest in AI applications, due to the lack of standardized and unified validation procedures and the heterogeneity of patient cohorts, its practical role in rhinology, particularly in radiological image processing in CRS, is not yet well defined, and further research is needed. It should be crucial for physicians to use their knowledge and skills to critically assess the information provided by AI and make any final treatment decisions.

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