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Artificial Intelligence-Based Pathological Subtype Diagnosis of Nasal Polyps: A Multidimensional and
Xin Luo1,2,3,4, Hansheng Li5, Jianning Chen6
1Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Background:
Nasal polyps (NP) are common upper respiratory conditions with diverse inflammatory subtypes influencing clinical features and prognosis. Manual counting of inflammatory cells in microscopic images (MI) is laborious and subjective, limiting diagnostic precision and treatment decisions.
Methods:
A total of 2457 slides from 20 hospitals were used to develop an AI-based NP subtype diagnosis system (NPSS). NPSS-MI was built using 1047 slides (15,705 MIs) annotated by pathologists. NPSS-WSI was trained on 1410 slides (21,150 images) combining PA-P2PNet for cell detection and U-KAN for region segmentation. Three-dimensional reconstruction (3DNP) using registration and point cloud analysis enabled spatial quantification of inflammatory cells. Twelve pathologists evaluated NPSS accuracy and efficiency on 200 slides, and recurrence prediction models were developed using logistic regression in a 131-patient cohort.
Results:
NPSS achieved performance with a weighted average F1-score of 0.809 for cell detection and an intersection over union (IoU) of 0.827 for region segmentation with an internal dataset. External dataset performance showed an F1-score of 0.792 and an IoU of 0.815. Forty randomly accumulated MIs were approximated WSI results. NPSS-MI and NPSS-WSI reached accuracies of 90% and 91%, reducing diagnostic time from 193 to 8 s and from 10,450 to 250 s, respectively. Junior pathologists using NPSS improved accuracy from 50% to 89%. Inflammatory cells showed distinct spatial patterns in 3DNP. NPSS-WSI prognostic model outperformed the MI-based model (AUC 86.64% vs. 79.81%, p = 0.039).
Conclusions:
NPSS integrates MI, WSI, and 3DNP to enable accurate and efficient NP subtype diagnosis and prognosis prediction, greatly enhancing diagnostic precision and clinical utility.
Insights
An AI system (NPSS) accurately diagnoses nasal polyp subtypes and predicts recurrence by integrating microscopic (MI) and whole-slide imaging (WSI). This AI tool significantly reduces diagnostic time and improves accuracy for pathologists.
Area of Science:
- Otorhinolaryngology
- Medical Imaging
- Artificial Intelligence
Background:
- Nasal polyps (NP) present diverse inflammatory subtypes impacting clinical outcomes.
- Manual cell counting in microscopic images (MI) is time-consuming and subjective.
- Current methods limit diagnostic precision for NP subtypes and treatment decisions.
Purpose of the Study:
- To develop an AI-based system (NPSS) for accurate NP subtype diagnosis.
- To enhance diagnostic efficiency and precision using artificial intelligence.
- To predict NP recurrence using integrated imaging data.
Main Methods:
- Developed NPSS using 2457 slides from 20 hospitals, incorporating MI and whole-slide imaging (WSI).
- Employed PA-P2PNet for cell detection and U-KAN for region segmentation in NPSS-WSI.
- Utilized 3D reconstruction (3DNP) for spatial cell quantification and logistic regression for recurrence prediction.
Main Results:
- NPSS achieved high performance (F1-score 0.809, IoU 0.827) on internal and external datasets.
- NPSS significantly reduced diagnostic time (e.g., WSI from 10,450s to 250s) and improved pathologist accuracy.
- The NPSS-WSI prognostic model showed superior predictive performance (AUC 86.64%) compared to MI-based models.
Conclusions:
- NPSS integrates MI, WSI, and 3DNP for precise NP subtype diagnosis and prognosis.
- The AI system enhances diagnostic efficiency and clinical utility in managing nasal polyps.
- NPSS represents a significant advancement in the objective assessment and management of nasal polyps.
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