A Study on the Significance of Nasal Smear Eosinophil Count and Blood Absolute Eosinophil Count in Patients with Allergic Rhinitis of Varied Severity of Symptoms

Sivaranjani, M.; Gopal, Srirupa Hari; M, R; K, Bajuk Studen; Ranjana, N.

doi:10.1007/s12070-023-03945-5

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Preprint1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images

Yue,

Zeng,

Lin

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images

Yue,

Zeng,

Lin

et al. 2024

npj Digit. Med.

View full text Add to dashboard Cite

A Deep Learning Based Smartphone Application for Early Detection of Nasopharyngeal Carcinoma Using Endoscopic Images

Yue,

Zeng,

Lin

et al. 2024

Preprint

View full text Add to dashboard Cite

Nasal endoscopy is crucial for the early detection of nasopharyngeal carcinoma (NPC), but its accuracy relies significantly on the physician's expertise, posing challenges for primary healthcare providers. Here, we retrospectively analysed 39,340 nasal endoscopic white-light images from three high-incidence NPC centres, utilising eight advanced deep learning models to develop an Internet-enabled smartphone application, "Nose-Keeper", that can be used for early detection of NPC and five prevalent nasal conditions and assessment of healthy individuals. Our App demonstrated a remarkable overall accuracy of 92.27% (95% Confidence Interval (CI): 90.66%-93.61%). Notably, its sensitivity and specificity in NPC detection achieved 96.39% and 99.91%, respectively, outperforming nine experienced otolaryngologists. Explainable artificial intelligence was employed to highlight key lesion areas, improving Nose-Keeper's decision-making accuracy and safety. Nose-Keeper can assist primary healthcare providers in diagnosing NPC and related diseases efficiently, offering a valuable resource for people in high-incidence NPC regions to manage nasal cavity health effectively.

show abstract

A Study on the Significance of Nasal Smear Eosinophil Count and Blood Absolute Eosinophil Count in Patients with Allergic Rhinitis of Varied Severity of Symptoms

Cited by 2 publications

References 13 publications

A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images

A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images

A Deep Learning Based Smartphone Application for Early Detection of Nasopharyngeal Carcinoma Using Endoscopic Images

Contact Info

Product

Resources

About