Medical data science in rhinology: Background and implications for clinicians

Jun, Young Joon; Jung, Joonho; Lee, Heung Man

doi:10.1016/j.amjoto.2020.102627

Cited by 4 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only recently (since 2015) a slow increase in their descriptions emerged in literature. In the majority of the rhinologic studies in which an AI approach was used, cluster analyses were performed, i.e., to predict surgical vs. medical treatments for CRS in patients who did not have successful outcomes after initial medical treatment ( 68 ). Regarding the ML technology, the majority of algorithms are divided into supervised or unsupervised learning.…”

Section: Technological Innovationmentioning

confidence: 99%

Pharmacological, Technological, and Digital Innovative Aspects in Rhinology

et al. 2021

View full text Add to dashboard Cite

Innovation refers to the introduction of a product, a process, a service or a solution resulting in something new or significantly improved compared to the already available alternatives. In the clinical context, it is strictly related to the identification of a new added value in terms of quality, therapeutic efficacy and safety. Over the years several innovative approaches have been introduced in the clinical practice, revolutionizing the treatment and the management of important rhinologic conditions. Innovative tools, including new drugs, biomaterials, and mobile applications seem to be able to improve the clinical outcomes and the quality of life of many patients affected by (often relapsing) rhinologic diseases. Among the main modern pharmacological innovations, mention must be made of the biological drugs like monoclonal antibodies (mAbs). Recently, new mAbs have been introduced and investigated as useful arms in the treatment of some inflammatory/infectious or oncological diseases affecting the nasal cavities and paranasal sinuses. The already approved or still investigated mAbs work inhibiting different type 2 inflammation pathways, including those mediated by IgE (omalizumab), IL-4/IL-13 (dupilumab), and IL-5 (mepolizumab). Moreover, considering the higher expression of PD-L1 in nasopharyngeal carcinoma, the use of PD-1 inhibitors, such as nivolumab, or a dual CTLA-4/PD-1 blockade (ipilimumab plus nivolumab) appear to be an effective strategy for the treatment of this cancer form. The implants with bio-absorbable biomaterials represent new interesting available technological innovations. Moreover, advanced technologies such as the artificial intelligence, the machine learning as well as the augmented or virtual reality have also proved useful in rhinologic field with main impacts on precision medicine and surgery. Finally, the development and use of mobile-Health tools represent a winning strategy in monitoring of the therapy success, safety and tolerability as well as the progress of chronic disease including chronic rhinosinusitis with nasal polyps. Supporting the research of innovative tools and strategies (including pharmacological, technologic, or digital ones) is essential to improve the management of chronic diseases that significantly affect the patients' quality of life.

show abstract

Section: Technological Innovationmentioning

confidence: 99%

Pharmacological, Technological, and Digital Innovative Aspects in Rhinology

et al. 2021

View full text Add to dashboard Cite

show abstract

Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features

Türker

Emre

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace

et al. 2023

Ear Nose Throat J

View full text Add to dashboard Cite

Background: Otorhinolaryngology diseases are well suited for artificial intelligence (AI)-based interpretation. The use of AI, particularly AI based on deep learning (DL), in the treatment of human diseases is becoming more and more popular. However, there are few bibliometric analyses that have systematically studied this field. Objective: The objective of this study was to visualize the research hot spots and trends of AI and DL in ENT diseases through bibliometric analysis to help researchers understand the future development of basic and clinical research. Methods: In all, 232 articles and reviews were retrieved from The Web of Science Core Collection. Using CiteSpace and VOSviewer software, countries, institutions, authors, references, and keywords in the field were visualized and examined. Results: The majority of these papers came from 44 nations and 498 institutions, with China and the United States leading the way. Common diseases used by AI in ENT include otosclerosis, otitis media, nasal polyps, sinusitis, and so on. In the early years, research focused on the analysis of hearing and articulation disorders, and in recent years mainly on the diagnosis, localization, and grading of diseases. Conclusions: The analysis shows the periodical hot spots and development direction of AI and DL application in ENT diseases from the time dimension. The diagnosis and prognosis of otolaryngology diseases and the analysis of otolaryngology endoscopic images have been the focus of current research and the development trend of future.

show abstract

Medical data science in rhinology: Background and implications for clinicians

Cited by 4 publications

References 19 publications

Pharmacological, Technological, and Digital Innovative Aspects in Rhinology

Pharmacological, Technological, and Digital Innovative Aspects in Rhinology

Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features

Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace

Contact Info

Product

Resources

About