Automated surgical skill assessment in endoscopic pituitary surgery using real-time instrument tracking on a high-fidelity bench-top phantom.

Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.

PitRSDNet: Predicting intra-operative remaining surgery duration in endoscopic pituitary surgery.

Accurate intra-operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration.

Bitter taste receptor T2R14-Gαi coupling mediates innate immune responses to microbial quorum sensing molecules in cystic fibrosis.

Cystic fibrosis (CF) is an autosomal recessive disease characterized by microbial infection and progressive decline in lung function, leading to significant morbidity and mortality. The bitter taste receptor T2R14 is a chemosensory receptor that is significantly expressed in airways. Using a combination of cell-based assays and T2R14 knockdown in bronchial epithelial cells from CF and non-CF individuals, we observed that T2R14 plays a crucial role in the detection of bacterial and fungal signals and enhances host innate immune responses.

Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.

Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition.

Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals.

Objective: This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team's ability to detect cerebral aneurysms with and without AI-assistance.

Background: Modern microscopic surgery enables the capture of operative video data on an unforeseen scale.