Stimulated Raman Histology Interpretation by Artificial Intelligence Provides Near-Real-Time Pathologic Feedback for Unprocessed Prostate Biopsies.

PURPOSE: Stimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology. MATERIALS AND METHODS: Unprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set. RESULTS: Prostate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%. CONCLUSIONS: Stimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.

Adaptive optics for enhanced signal in CARS microscopy.

We report the use of adaptive optics with coherent anti-Stokes Raman scattering (CARS) microscopy for label-free deep tissue imaging based on molecular vibrational spectroscopy. The setup employs a deformable membrane mirror and a random search optimization algorithm to improve signal intensity and image quality at large sample depths. We demonstrate the ability to correct for both system and sample-induced aberrations in test samples as well as in muscle tissue in order to enhance the CARS signal. The combined system and sample-induced aberration correction increased the signal by an average factor of approximately 3x for the test samples at a depth of 700 microm and approximately 6x for muscle tissue at a depth of 260 microm. The enhanced signal and higher penetration depth offered by adaptive optics will augment CARS microscopy as an in vivo and in situ biomedical imaging modality.