University of Arizona researchers combined label-free multiphoton microscopy with neural networks to accurately classify pancreatic neuroendocrine neoplasms in tissue samples.
Key Details
- 1Multiphoton microscopy (MPM) was used to image pancreatic neuroendocrine neoplasm (PNEN) samples without labeling.
- 2Researchers trained both traditional machine learning and four convolutional neural networks (CNNs) on these images.
- 3CNNs achieved classification accuracies ranging from 90.8% to 96.4%, outperforming the ML algorithm’s 80.6%.
- 4Analysis showed key features included collagen content and image texture metrics.
- 5The approach is faster than traditional pathology and was validated across samples from multiple biorepositories.
- 6Publication: Biophotonics Discovery, October 2, 2025, DOI: 10.1117/1.BIOS.2.4.045001.
Why It Matters
This study demonstrates the potential of advanced imaging and AI for real-time, highly accurate tumor detection, which could transform intraoperative pathology and improve surgical outcomes in oncology. Faster, label-free diagnosis may reduce delays and error in tumor identification during surgery.
Source
EurekAlert
Related News
•EurekAlert
ML and Multimodal Imaging Power Cerebral Blood Flow Monitoring for Spaceflight
Researchers developed a machine learning model that uses ultrasound and MRI data to predict cerebral blood flow in simulated microgravity for astronaut health.
•EurekAlert
Deep Learning Model Predicts Language Outcomes After Cochlear Implants Using MRI
AI model using deep transfer learning accurately predicts spoken language outcomes in deaf children after cochlear implantation based on pre-implantation brain MRI scans.
•EurekAlert
AI Model Accurately Predicts Blood Loss Risk in Liposuction
A machine learning model predicts blood loss during high-volume liposuction with 94% accuracy.