Partially automated robotic systems, such as camera holders, represent a pivotal step towards enhancing efficiency and precision in surgical procedures. Therefore, this paper introduces an approach for real-time tool localization in laparoscopy surgery using convolutional neural networks. The proposed model, based on two Hourglass modules in series, can localize up to two surgical tools simultaneously. This study utilized three datasets: the ITAP dataset, alongside two publicly available datasets, namely Atlas Dione and EndoVis Challenge. Three variations of the Hourglass-based models were proposed, with the best model achieving high accuracy (92.86%) and frame rates (27.64 FPS), suitable for integration into robotic systems. An evaluation on an independent test set yielded slightly lower accuracy, indicating limited generalizability. The model was further analyzed using the Grad-CAM technique to gain insights into its functionality. Overall, this work presents a promising solution for automating aspects of laparoscopic surgery, potentially enhancing surgical efficiency by reducing the need for manual endoscope manipulation.

Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (\'you only look once\') and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings.

OBJECTIVE: Use microscopic video-based tracking of laryngeal surgical instruments to investigate the effect of robot assistance on instrument tremor.
METHODS: Experimental trial.
METHODS: Tertiary Academic Medical Center.
METHODS: In this randomized cross-over trial, 36 videos were recorded from 6 surgeons performing left and right cordectomies on cadaveric pig larynges. These recordings captured 3 distinct conditions: without robotic assistance, with robot-assisted scissors, and with robot-assisted graspers. To assess tool tremor, we employed computer vision-based algorithms for tracking surgical tools. Absolute tremor bandpower and normalized path length were utilized as quantitative measures. Wilcoxon rank sum exact tests were employed for statistical analyses and comparisons between trials. Additionally, surveys were administered to assess the perceived ease of use of the robotic system.
RESULTS: Absolute tremor bandpower showed a significant decrease when using robot-assisted instruments compared to freehand instruments (P = .012). Normalized path length significantly decreased with robot-assisted compared to freehand trials (P = .001). For the scissors, robot-assisted trials resulted in a significant decrease in absolute tremor bandpower (P = .002) and normalized path length (P < .001). For the graspers, there was no significant difference in absolute tremor bandpower (P = .4), but there was a significantly lower normalized path length in the robot-assisted trials (P = .03).
CONCLUSIONS: This study demonstrated that computer-vision-based approaches can be used to assess tool motion in simulated microlaryngeal procedures. The results suggest that robot assistance is capable of reducing instrument tremor.

Unresectable liver tumors are commonly treated with percutaneous radiofrequency ablation (RFA). However, this technique is associated with high recurrence rates due to incomplete tumor ablation. Accurate image guidance of the RFA procedure contributes to successful ablation, but currently used imaging modalities have shortcomings in device guidance and treatment monitoring. We explore the potential of using photoacoustic (PA) imaging combined with conventional ultrasound (US) imaging for real-time RFA guidance. To overcome the low penetration depth of light in tissue, we have developed an annular fiber probe (AFP), which can be inserted into tissue enabling interstitial illumination of tissue. The AFP is a cannula with 72 optical fibers that allows an RFA device to slide through its lumen, thereby enabling PA imaging for RFA device guidance and ablation monitoring. We show that the PA signal from interstitial illumination is not affected by absorber-to-surface depth compared to extracorporeal illumination. We also demonstrate successful imaging of the RFA electrodes, a blood vessel mimic, a tumor-mimicking phantom, and ablated liver tissue boundaries in ex vivo chicken and bovine liver samples. PA-assisted needle guidance revealed clear needle tip visualization, a notable improvement to current US needle guidance. Our probe shows potential for RFA device guidance and ablation detection, which potentially aids in real-time monitoring.