The application of real-time visual tracking in laparoscopic surgery has gained significant attention in recent years, driven by the growing demand for precise and automated surgical assistance. Instrument tracking, in particular, is critical for enhancing the safety and efficacy of minimally invasive surgery, where direct visibility is often limited. Real-time tracking of surgical instruments allows for more accurate maneuvering, reduces the risk of accidental tissue damage, and enables the development of advanced computer-assisted surgical systems. In this context, advancements in deep learning, particularly through detection models and modern tracking algorithms, have opened new avenues for addressing the challenges posed by real-time laparoscopic instrument tracking. However, according to the preliminary results, the existing combination of the detection model and tracking algorithm could not often handle the remaining challenges, including fast-motion speed, occlusion, overlapping, and close proximity of surgical instruments. This paper proposes a novel two-point association approach for surgical instrument tracking using a combination of YOLOv11 for object detection and refined ByteTrack for tracking. The proposed system is evaluated on a comprehensive dataset of surgical videos. The experimental results demonstrate superior performance in terms of segmentation accuracy (via F1-score), tracking robustness (via MOTA and HOTA), and real-time processing speed (via FPS). In order to validate the effectiveness of this research, real-time surgical instrument tracking is performed with the streaming of laparoscopic gynecologic surgery on a donated soft-tissue cadaver. The results indicate that the proposed system significantly improves the segmentation and tracking of surgical instruments, offering a reliable tool for enhancing intraoperative navigation and reducing the risk of surgical errors. This work contributes to the advancement of intelligent surgical systems, providing a foundation for further integration of machine learning techniques in the operating room.

As Computer Vision technology has evolved rapidly these days, the implementation of object detection and instance segmentation has been presented in various areas. In computer-aided laparoscopic surgery, the segmentation of surgical instruments is one of the active research areas. This paper presents the implementation and comparative analysis of a real-time surgical instruments segmentation system by incorporating ByteTrack, a powerful object tracking system, within the YOLOv8, a state-of-the-art Deep Learning algorithm for object detection and segmentation, together with an instruments gesture analysis of the practical results. The instrument gestures have been categorized into separating, crossing, and overlapping cases according to the most common instrument gestures during the surgery. The datasets from the ROBUST-MIS 2019 challenge have been applied and annotated for training, validating, and blind testing in this study. Considering trade-offs among model complexity, speed, and accuracy, the medium version (YOLOv8m) has been chosen in this study for its comparative model complexity, design for working in real-time, and relative high accuracy. In order to validate the effectiveness of this research, real-time segmentation of surgical instruments has been performed with the streaming of laparoscopic gynecologic surgery on 5 donated soft-tissue cadaver cases. According to the experimental results, although YOLOv8 can provide very high-accuracy evaluation metrics for both F1-score and mAP (mean Average Precision), the segmentation accuracy results could have been further improved by incorporating the ByteTrack within the YOLOv8 algorithm. Owing to the 2-association scheme that has been designed for object tracking in ByteTrack, referring to the tracklet from the previous frame could recover missed segmentations that come with too low confidence values. The findings identify that the Modified model of incorporating ByteTrack with YOLOv8 could improve the F1-score from 0.89 to 0.92, which outperformed all of the previous studies on the ROBUST-MIS 2019 Challenge, and from 0.82 to 0.88 on the blinded captured dataset from live streaming videos with a real-time segmentation speed of approximately 45 FPS (Frames Per Second), which is sufficient for a real-time application as opposed to 60 FPS from only the YOLOv8 algorithm. From the instrument gestures result analysis, ByteTrack could improve the segmentation performance in all gesture categories: separating, crossing, and overlapping. However, the remaining segmentation failures mostly lie in crossing and overlapping gestures.

In this work, we report a low-cost and highly sensitive electrochemical sensor for detecting As(III) in water. The sensor uses a 3D microporous graphene electrode with nanoflowers, which enriches the reactive surface area and thus enhances its sensitivity. The detection range achieved was 1–50 ppb, meeting the US-EPA cutoff criteria of 10 ppb. The sensor works by trapping As(III) ions using the interlayer dipole between Ni and graphene, reducing As(III), and transferring electrons to the nanoflowers. The nanoflowers then exchange charges with the graphene layer, producing a measurable current. Interference by other ions, such as Pb(II) and Cd(II), was found to be negligible. The proposed method has potential for use as a portable field sensor for monitoring water quality to control hazardous As(III) in human life.

Nowadays, with the current technological forces that have been shaping our bright future, one of these is Computer Vision. And this statement is true across various matters, including laparoscopic gynecology, where computer-aided procedures for object recognition could offer surgeons the opportunity to ease up on on-going surgeries and/or to practice their surgical skills with offline surgeries. However, most of the previous work has been retrospective and focused on methodology from a computational viewpoint with minimal datasets showing how Computer Vision can be utilized for laparoscopic surgery. The main purpose of this paper is not just to evaluate state-of-the-art object detection models for uterus detection, but also to emphasize clinical application via the collaboration between surgeons and peopleware which is important in the further development and adoption of this technology, leading to improved clinical outcomes in Laparoscopic Gynecology. Two experiment phases have been conducted. Phase#1 applied 8 different Deep Learning models for uterus detection and were tested on the dataset, obtained from 40 public YouTube videos in Laparoscopic Gynecologic Surgery. In order to prove this new technology before performing on patients, and also due to the ethics of human experimentation, extensive testing on soft-tissue cadavers has been used, hence Phase#2 which performed the best models from the first experiment phase serving a real-time streaming feed during 4 soft-tissue cadaver laparoscopic surgeries, which theoretically is the best approach as they are the closest to humans in terms of shape and structure. Four models, pre-trained on the COCO 2017 Dataset on TensorFlow Model Zoo: CenterNet; EfficientDet; SSD; and Faster R-CNN; plus YOLOv4 on Darknet Framework, along with YOLOv4, YOLOv5 and YOLOv7 on Pytorch have been scrutinized here. The inference time (in FPS: Frame Per Second), F1-score and AP (Average Precision) have been used as evaluation metrics. The results exhibited that all 3 YOLOs on PyTorch outperformed all effectiveness metrics, including with great inference speed which is suitable for real-time surgeries. Lastly, a by-product but also useful contribution of this work, is the annotated dataset on uterus detection from both public videos and live feed on cadaver surgeries.

Sugarcane stem borer poses a significant threat to sugarcane production, causing extensive damage and economic losses in the agricultural sector. Accurately predicting the destruction rate caused by this pest is crucial for implementing timely and effective management strategies. This paper presents the development and implementation of a time series prediction system designed to estimate the short-time destruction rate of sugarcane in the near future caused by stem borer infestations. The proposed Hybrid system combines Adaptive and Batch machine learning techniques, allowing for continuous model updates and adaptation to new data while maintaining initial accuracy, thereby improving predictive accuracy over time. A dataset comprising historical pest infestation data, manually recorded from the fields, and environmental factors, retrieved online, was used to train the model to capture complex temporal dependencies, with features such as temperature, humidity, seasonal patterns, and pest density being key predictors. The performance of the model was evaluated using the MAE, AIC, BIC, and HQIC, which demonstrated its robustness and reliability in predicting the destruction rate. This proposed Hybrid prediction system offers a valuable tool for sugarcane farmers and agronomists, enabling proactive decision-making to mitigate the impact of stem borer infestations. The findings of this study contribute to the broader application of machine learning in agricultural pest management, highlighting the potential for Hybrid learning approaches to enhance prediction accuracy in dynamic environments.

Laparoscopic tubal sterilization is a widely used method for permanent contraception, where accurately identifying the uterus and fallopian tubes is crucial. However, this process becomes more difficult when these structures are obscured or partially hidden during surgery. Conventional imaging techniques often struggle to accurately identify anatomical features in such scenarios. In contrast, recent progress in deep learning, particularly with the You Only Look Once (YOLO) algorithm, has shown promising results by enabling real-time object detection in medical imaging. By incorporating KeyPoint detection with the newest YOLOv10 version, the system’s ability to detect and pinpoint anatomical landmarks is significantly improved, even when occlusions are present. This study presents a novel approach that merges YOLOv10 with KeyPoint detection to reliably identify the uterus and fallopian tubes in real-time laparoscopic footage, even when these structures are partially hidden. The method also identifies the Ampulla and Isthmus at the Uterine-Tubal junction, even in cases where visualization is obstructed. Additionally, an ellipse estimation technique is applied to further enhance detection under occluded conditions by estimating the uterus’s position and orientation when it is not fully visible. The results highlight the potential for real-time application of this system in clinical settings, marking a step forward in AI-assisted laparoscopic surgery.