Publications by authors named "D Reinecke"
Background: Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited.
View Article and Find Full Text PDFObjective: The purpose of this study was to evaluate the effectiveness of a deep learning model (DLM) in improving the sensitivity of neurosurgery residents to detect intracranial aneurysms on CT angiography (CTA) in patients with aneurysmal subarachnoid hemorrhage (aSAH).
Methods: In this diagnostic accuracy study, a set of 104 CTA scans of aSAH patients containing a total of 126 aneurysms were presented to three blinded neurosurgery residents (a first-year, third-year, and fifth-year resident), who individually assessed them for aneurysms. After the initial reading, the residents were given the predictions of a dedicated DLM previously established for automated detection and segmentation of intracranial aneurysms.
Article Synopsis
- Accurate intraoperative diagnosis of primary CNS lymphoma (PCNSL) is vital for surgical decisions but is challenging due to similar features with other CNS diseases; a new method combines stimulated Raman histology (SRH) with deep learning to improve this process.
- The RapidLymphoma system uses a portable Raman microscope to create virtual images of tissue samples in under three minutes and employs a deep learning model trained on 54,000 images, allowing it to detect PCNSL and differentiate it from other conditions effectively.
- In testing, RapidLymphoma achieved a high accuracy rate of 97.81%, performing better than traditional methods, and demonstrated its capability to identify specific histological features crucial for diagnosis, providing quick feedback
Article Synopsis
- Accurate intraoperative diagnosis of primary CNS lymphoma (PCNSL) is challenging due to overlapping features with other CNS conditions, but a new method combining stimulated Raman histology (SRH) and deep learning seeks to improve this.
- The deep learning system, RapidLymphoma, analyzes unprocessed tissue samples quickly, achieving high accuracy in distinguishing PCNSL from other entities, with an overall accuracy of 97.81% in a test cohort.
- RapidLymphoma not only provides rapid diagnostic results but also visual feedback, aiding surgical decision-making and potential treatment strategies within a critical timeframe.
Purpose: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy.
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