Id: 3526830 Artificial Intelligence Based Rapid Onsite Cytopathology Evaluation (Rose-Aidtm) vs. Physician Interpretation of Cytopathology Images of Endoscopic Ultrasound-Guided Fine-Needle Aspiration (Eus-Fna) of Pancreatic Solid Lesions

A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: A retrospective, multicenter, diagnostic study

“…found that their deep learning model achieved an accuracy of 0·87 in pancreatic cancer identification and ranked 4 th among the included 19 observers. 18 Notably, they found that the deep learning model outperformed all 6 interventional gastroenterologists. Thosani et al.…”

“… 14 , 15 , 16 Three preliminary conference abstracts have implemented deep learning in the pathological classification of pancreatic solid masses using a relatively small sample size of cytopathological slides from EUS-FNA and achieved limited diagnostic performance in single-center validation. 17 , 18 , 19 However, it is more clinically applicable for a deep learning-based system to be capable of segmenting the cell clusters and be validated on datasets from multiple hospitals. To the best of our knowledge, no study has adopted DCNN-based segmentation algorithms for evaluating sample adequacy and identifying cancer cell clusters in cytopathological slides from EUS-FNA.…”

Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges

“…Recent domestic and international studies demonstrate that AI's diagnostic accuracy for DP images is comparable to that of senior pathologists, providing faster, more accurate, efficient, and collaborative pathological diagnoses [39,54,55]. AI can be particularly valuable in supporting clinical pathological diagnoses when pathologists are unavailable.…”

“…Additionally, a few original research papers and draft conference abstracts on the pathological classification of solid pancreatic masses were published. These papers used a small sample of cytopathological slides obtained through EUS-FNA, which had a limited diagnostic performance in single-center validation (accuracy range: 80-94%) [54,55,62]. Hyperspectral imaging (HSI) is a new optical diagnostic technology that combines spectroscopy.…”