The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach that significantly enhances the detection of ganglion cells in frozen sections. The data set comprises 366 frozen and 302 formalin-fixed-paraffin-embedded hematoxylin and eosin-stained slides obtained from 164 patients from 3 centers.

A robust image segmentation and synthesis pipeline for histopathology.

Significant diagnostic variability between and within observers persists in pathology, despite the fact that digital slide images provide the ability to measure and quantify features much more precisely compared to conventional methods. Automated and accurate segmentation of cancerous cell and tissue regions can streamline the diagnostic process, providing insights into the cancer progression, and helping experts decide on the most effective treatment. Here, we evaluate the performance of the proposed PathoSeg model, with an architecture comprising of a modified HRNet encoder and a UNet++ decoder integrated with a CBAM block to utilize attention mechanism for an improved segmentation capability.

Efficiency of oral keratinized gingiva detection and measurement based on convolutional neural network.

Background: With recent advances in artificial intelligence, the use of this technology has begun to facilitate comprehensive tissue evaluation and planning of interventions. This study aimed to assess different convolutional neural networks (CNN) in deep learning algorithms to detect keratinized gingiva based on intraoral photos and evaluate the ability of networks to measure keratinized gingiva width.

Methods: Six hundred of 1200 photographs taken before and after applying a disclosing agent were used to compare the neural networks in segmenting the keratinized gingiva.

Nanostructured Metal Oxide-Based Electrochemical Biosensors in Medical Diagnosis.

Nanostructured metal oxides (NMOs) provide electrical properties such as high surface-to-volume ratio, reaction activity, and good adsorption strength. Furthermore, they serve as a conductive substrate for the immobilization of biomolecules, exhibiting notable biological activity. Capitalizing on these characteristics, they find utility in the development of various electrochemical biosensing devices, elevating the sensitivity and selectivity of such diagnostic platforms.