Every now and then, we witness significant improvements in the performance of Deep Learning models. A typical cycle of improvement involves enhanced accuracy followed by reduced computing time. As algorithms get better at their job, it is worthwhile to try to evaluate their performance on problems that are affected by them. Computationally intense problems, such as object detection for Computer Aided Laparoscopy (CAL), can benefit from such improvements in such technologies. Recently a new set of variants of You Look Only Once (YOLO) models based on Neural Architecture Search (NAS) technique have been released. Deci, the enterprise behind this new development, touts a much better performance both in terms of accuracy as well as computational efficiency. In this paper, we have analyzed the performance YOLO-NAS on a well-known benchmark dataset related to CAL. We found that the performance of all the NAS-based YOLO was inferior as compared to other State-of-the-Art (SoTA) YOLO models. We compare our results against the YOLOv7 model too.

Of an order of 330 million surgeries are performed worldwide every year. Yet there is a backlog of around 150 million pending surgeries annually. Surgical robotics is becoming a lot more sophisticated by the day. Much of this increasing success is due to advances in Computer Vision (CV) . CV allows the tracking of tools, detection of organs, and a description of the phase of surgery that enables a surgeon to perform the delicate art of surgery with much greater precision and efficiency. These advancements have also enabled remote robotic surgery in which a patient and the surgeon can be far apart from each other geographically. In this paper, we report promising results of evolving the architecture of capsule networks for tool classification for Computer-Aided Laparoscopy (CAL).

As algorithms get better at their accuracy and computational efficiency, they invoke curiosity among the affected scientific communities to check if they can benefit from newer versions or not. Computer vision is one such domain that has observed rapid growth in terms of algorithmic advancements. The advent of deep learning was itself a catalyst for agile innovation. Coupled with rapid improvements in algorithms for object detection, the speed of innovation has become tremendous. And so there are many engineering and scientific disciplines that leverage object detection, any improvement in the latter has a ripple effect on the erstwhile. Computer Aided Laparoscopy (CAL) has come a long way due to object detection algorithms based on deep learning. Yet, every once in a while a new algorithm is released. It is tempting to see how a new algorithm may have affected the tool detection accuracy and efficiency in CAL. Recently version 8 of the famous You Look Only Once (YOLO) algorithm was released. Like all the past releases, it has been claimed that this version is better at detection accuracy as well as computational efficiency. This paper examines the performance of YOLOv8 at tool detection in a CAL context. We employed a well-known laparoscopy tool detection benchmark dataset in this research. Models with superior performance have been obtained as a result of this research. Models are superior in terms of both detection accuracy as well as inference speed. Moreover, the models are ready to be deployed into a production environment. The results that are reported in this paper are not only useful for the surgical community but also for the benchmarking of the YOLO algorithm.