Real-time Artificial Intelligence Research Articles

Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence.

Most intraoperative adverse events (iAEs) result from surgeons' errors, and bleeding is the majority of iAEs. Recognizing active bleeding timely is important to ensure safe surgery, and artificial intelligence (AI) has great potential for detecting active bleeding and providing real-time surgical support. This study aimed to develop a real-time AI model to detect active intraoperative bleeding. We extracted 27 surgical videos from a nationwide multi-institutional surgical video database in Japan and divided them at the patient level into three sets: training (n = 21), validation (n = 3), and testing (n = 3). We subsequently extracted the bleeding scenes and labeled distinctively active bleeding and blood pooling frame by frame. We used pre-trained YOLOv7_6w and developed a model to learn both active bleeding and blood pooling. The Average Precision at an Intersection over Union threshold of 0.5 (AP.50) for active bleeding and frames per second (FPS) were quantified. In addition, we conducted two 5-point Likert scales (5 = Excellent, 4 = Good, 3 = Fair, 2 = Poor, and 1 = Fail) questionnaires about sensitivity (the sensitivity score) and number of overdetection areas (the overdetection score) to investigate the surgeons' assessment. We annotated 34,117 images of 254 bleeding events. The AP.50 for active bleeding in the developed model was 0.574 and the FPS was 48.5. Twenty surgeons answered two questionnaires, indicating a sensitivity score of 4.92 and an overdetection score of 4.62 for the model. We developed an AI model to detect active bleeding, achieving real-time processing speed. Our AI model can be used to provide real-time surgical support.

Federated Learning With Dynamic Epoch Adjustment and Collaborative Training in Mobile Edge Computing

As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. Firstly, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

Preserving paradata for accountability of semi-autonomous AI agents in dynamic environments: An archival perspective

This paper proposes the category of real-time artificial intelligence (AI) systems as applications of computerized control systems in dynamic, time-constrained contexts normally managed by human intelligence. Noting the accountability challenges which these systems introduce, the paper posits the need for robust documentation and records capacities within these systems. The paper surveys four real-time AI systems with significant records needs: autonomous vehicles, online content targeting systems, mixed-reality tools for surgical contexts, and digital twin systems in airport facilities management. The paper identifies paradata, or the data leading up to an output in a system's operation, as a key data category necessitating preservation for full transparency in the records generated by these systems. Paradata is defined as “information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.” Paradata uncovers opaque technological processes underlying the production of other datasets and at a granular level must be identified and preserved to delineate the boundaries between human and system agency in semi-autonomous systems. With a basis in control theory, the paper finally offers a framework for assessing the functions of real-time AI systems' operations and their documentation and records needs.

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors.

At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.

Optimizing Cloud Infrastructure for Real-time AI Processing: Challenges and Solutions

This research paper explores the optimization of cloud infrastructure for real-time artificial intelligence (AI) processing, addressing challenges, solutions, and implications from various perspectives. It discusses scalability issues, latency concerns, resource allocation, security considerations, and cost optimization challenges faced by organizations deploying AI workloads in the cloud. Case studies from diverse industries showcase the tangible benefits of implementing scalable architectures, edge computing integration, specialized hardware utilization, containerization, and data caching techniques. The paper also examines ethical and societal implications, including data privacy, bias, accountability, job displacement, and access disparities. An international perspective highlights regional variations in infrastructure availability, regulatory differences, cultural attitudes, collaboration efforts, and economic impacts. The discussion emphasizes the importance of addressing these challenges while harnessing the economic development opportunities offered by cloud infrastructure optimization for real-time AI processing.

Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors.

High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.

Navigating Now and Next: Recent Advances and Future Horizons in Robotic Radical Prostatectomy.

Robotic-assisted radical prostatectomy (RARP) has become the leading approach for radical prostatectomy driven by innovations aimed at improving functional and oncological outcomes. The initial advancement in this field was transperitoneal multiport robotics, which has since undergone numerous technical modifications. These enhancements include the development of extraperitoneal, transperineal, and transvesical approaches to radical prostatectomy, greatly facilitated by the advent of the Single Port (SP) robot. This review offers a comprehensive analysis of these evolving techniques and their impact on RARP. Additionally, we explore the transformative role of artificial intelligence (AI) in digitizing robotic prostatectomy. AI advancements, particularly in automated surgical video analysis using computer vision technology, are unprecedented in their scope. These developments hold the potential to revolutionize surgeon feedback and assessment and transform surgical documentation, and they could lay the groundwork for real-time AI decision support during surgical procedures in the future. Furthermore, we discuss future robotic platforms and their potential to further enhance the field of RARP. Overall, the field of minimally invasive radical prostatectomy for prostate cancer has been an incubator of innovation over the last two decades. This review focuses on some recent developments in robotic prostatectomy, provides an overview of the next frontier in AI innovation during prostate cancer surgery, and highlights novel robotic platforms that may play an increasing role in prostate cancer surgery in the future.

Detection of sessile serrated adenoma using artificial intelligence-enhanced endoscopy: an Asian perspective.

As the serrated pathway has gained prominence as an alternative colorectal carcinogenesis pathway, sessile serrated adenomas or polyps (SSA/P) have been highlighted as lesions to rule out during colonoscopy. These lesions are however morphologically difficult to detect on endoscopy and can be mistaken for hyperplastic polyps due to similar endoscopic features. With the underlying nature of rapid progression and malignant transformation, interval cancer is a likely consequence of undetected or overlooked SSA/P. Real-time artificial intelligence (AI)-assisted colonoscopy via the computer-assisted detection system (CADe) is an increasingly useful tool in improving adenoma detection rate by providing a second eye during the procedure. In this article, we describe a guide through a video to illustrate the detection of SSA/P during AI-assisted colonoscopy. Consultant-grade endoscopists utilized real-time AI-assisted colonoscopy device, as part of a larger prospective study, to detect suspicious lesions which were later histopathologically confirmed to be SSA/P. All lesions were picked up by the CADe where a real-time green box highlighted suspicious polyps to the clinician. Three SSA/P of varying morphology are described with reference to classical SSA/P features and with comparison to the features of the hyperplastic polyp found in our study. All three SSA/P observed are in keeping with the JNET Classification (Type 1). In conclusion, CADe is a most useful aid to clinicians during endoscopy in the detection of SSA/P but must be complemented with factors such as good endoscopy skill and bowel prep for effective detection, and biopsy coupled with subsequent accurate histological diagnosis.

AI-powered real-time annotations during urologic surgery: The future of training and quality metrics

Introduction and objectiveReal-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time. Summary background dataWhile AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied. MethodsWe conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time. ResultsReal-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage. ConclusionsFor the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education.

Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule.

Artificial intelligence (AI) prediction is increasingly used for decision making in health care, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect. Data from 8274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real-time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP). The mean ± SD age was 56.1 ± 16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC 0.961) and intensive care unit (ICU) admission (AUC 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP 0.785), ICU admission (BISAP 0.778), and mortality (BISAP 0.817). The first real-time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

Early-stage clinical evaluation of real-time artificial intelligence assistance for laparoscopic cholecystectomy.

Lay Summary The growing availability of surgical digital data and developments in analytics such as artificial intelligence (AI) are being harnessed to improve surgical care. However, technical and cultural barriers to real-time intraoperative AI assistance exist. This early-stage clinical evaluation shows the technical feasibility of concurrently deploying several AIs in operating rooms for real-time assistance during procedures. In addition, potentially relevant clinical applications of these AI models are explored with a multidisciplinary cohort of key stakeholders.

Real-time artificial intelligence enhanced defect engineering in CeO2 nanostructures

CeO2 nanostructures have been utilized for various biomedical, sensor, and catalysis applications owing to their unique defect structure, enabling them to have regenerative oxidative properties. Defect engineering in CeO2 nanostructures has major importance, enabling them to be utilized for specific applications. Despite various synthesis methods, it is challenging to have precise and reversible control over defect structures. Against this backdrop, in the current work, we have explored machine learning (ML) enhanced defect engineering of CeO2 nanofilms. In our earlier work [J. Vac. Sci. Technol. A 39, 060405 (2021)], we have developed an atomic layer deposition process for CeO2 using in situ ellipsometry measurements. In the current work, data collected through in situ spectroscopic ellipsometry and ex situ XPS have been correlated using two ML algorithms (gradient boost and random forest regressor) to exert better control over the chemical properties. Defect structures are one of the desired properties in CeO2 nanomaterials, characterized by the Ce3+/Ce4+ oxidation state ratio leading to its regenerative properties. We have shown that the defect structure of the CeO2 nanofilms can be predicted using in situ ellipsometry data in real time using a trained ML algorithm using two different methods. The first method involves an indirect approach of thickness prediction using an ML algorithm (k-nearest neighbors) followed by Ce3+/Ce4+ estimation using an experimental calibration curve. The second method with a more direct approach involves Ce3+/Ce4+ prediction using real-time ellipsometry data (amplitude ratio ψ and phase difference Δ) using gradient boost and random forest regressor.

Automatic measurements of left ventricular volumes and ejection fraction by artificial intelligence: clinical validation in real time and large databases.

Echocardiography is a cornerstone in cardiac imaging, and left ventricular (LV) ejection fraction (EF) is a key parameter for patient management. Recent advances in artificial intelligence (AI) have enabled fully automatic measurements of LV volumes and EF both during scanning and in stored recordings. The aim of this study was to evaluate the impact of implementing AI measurements on acquisition and processing time and test-retest reproducibility compared with standard clinical workflow, as well as to study the agreement with reference in large internal and external databases. Fully automatic measurements of LV volumes and EF by a novel AI software were compared with manual measurements in the following clinical scenarios: (i) in real time use during scanning of 50 consecutive patients, (ii) in 40 subjects with repeated echocardiographic examinations and manual measurements by 4 readers, and (iii) in large internal and external research databases of 1881 and 849 subjects, respectively. Real-time AI measurements significantly reduced the total acquisition and processing time by 77% (median 5.3 min, P < 0.001) compared with standard clinical workflow. Test-retest reproducibility of AI measurements was superior in inter-observer scenarios and non-inferior in intra-observer scenarios. AI measurements showed good agreement with reference measurements both in real time and in large research databases. The software reduced the time taken to perform and volumetrically analyse routine echocardiograms without a decrease in accuracy compared with experts.

Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time

BackgroundHyperglycemic crises are associated with high morbidity and mortality. Previous studies have proposed methods to predict adverse outcomes of patients in hyperglycemic crises; however, artificial intelligence (AI) has never been used to predict adverse outcomes. We implemented an AI model integrated with the hospital information system (HIS) to clarify whether AI could predict adverse outcomes.MethodsWe included 2,666 patients with hyperglycemic crises from emergency departments (ED) between 2009 and 2018. The patients were randomized into a 70%/30% split for AI model training and testing. Twenty-two feature variables from the electronic medical records were collected. The performance of the multilayer perceptron (MLP), logistic regression, random forest, Light Gradient Boosting Machine (LightGBM), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms was compared. We selected the best algorithm to construct an AI model to predict sepsis or septic shock, intensive care unit (ICU) admission, and all-cause mortality within 1 month. The outcomes between the non-AI and AI groups were compared after implementing the HIS and predicting the hyperglycemic crisis death (PHD) score.ResultsThe MLP had the best performance in predicting the three adverse outcomes, compared with the random forest, logistic regression, SVM, KNN, and LightGBM models. The areas under the curves (AUCs) using the MLP model were 0.852 for sepsis or septic shock, 0.743 for ICU admission, and 0.796 for all-cause mortality. Furthermore, we integrated the AI predictive model with the HIS to assist decision making in real time. No significant differences in ICU admission or all-cause mortality were detected between the non-AI and AI groups. The AI model performed better than the PHD score for predicting all-cause mortality (AUC 0.796 vs. 0.693).ConclusionsA real-time AI predictive model is a promising method for predicting adverse outcomes in ED patients with hyperglycemic crises. Further studies recruiting more patients are warranted.

Early monitoring-to-warning Internet of Things system for emerging infectious diseases via networking of light-triggered point-of-care testing devices.

Early monitoring and warning arrangements are effective ways to distinguish infectious agents and control the spread of epidemic diseases. Current testing technologies, which cannot achieve rapid detection in the field, have a risk of slowing down the response time to the disease. In addition, there is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. Motivated by these clinical needs, a sample-to-answer genetic diagnosis platform based on light-controlled capillary modified with a photocleavable linker is first developed, which could perform nucleic acid separation and release by light irradiation in less than 30seconds. Then, on site polymerase chain reaction was performed in a handheld closed-loop convective system. Test reports are available within 20 min. Because this method is portable, rapid, and easy to operate, it has great potential for point-of-care testing. Additionally, through multiple device networking, a real-time artificial intelligence monitoring system for pathogens was developed on a cloud server. Through data reception, analysis, and visualization, the system can send early warning signals for disease control and prevention. Thus, anti-epidemic measures can be implemented effectively, and deploying and running this system can improve the capabilities for the prevention and control of infectious diseases.

Machine learning in clinical practice: Evaluation of an artificial intelligence tool after implementation.

Artificial intelligence (AI) has gradually found its way into healthcare, and its future integration into clinical practice is inevitable. In the present study, we evaluate the accuracy of a novel AI algorithm designed to predict admission based on a triage note after clinical implementation. This is the first of such studies to investigate real-time AI performance in the emergency setting. The novel AI algorithm that predicts admission using a triage note was translated into clinical practice and integrated within St Vincent's Hospital Melbourne's electronic emergency patient management system. The data were collected from 1 January 2021 to 17 August 2022 to evaluate the diagnostic accuracy of the AI system after implementation. A total of 77 125 ED presentations were included. The live AI algorithm has a sensitivity of 73.1% (95% confidence interval 72.5-73.8), specificity of 74.3% (73.9-74.7), positive predictive value of 50% (49.6-50.4) and negative predictive value of 88.7% (88.5-89) with a total accuracy of 74% (73.7-74.3). The accuracy of the system was at the lowest for admission to psychiatric units (34%) and at the highest for gastroenterology and medical admission (84% and 80%, respectively). Our study showed the diagnostic evaluation of a real-time AI clinical decision-support tool became less accurate than the original. Although real-time sensitivity and specificity of the AI tool was still acceptable as a decision-support tool in the ED, we propose that continuous training and evaluation of AI-enabled clinical support tools in healthcare are conducted to ensure consistent accuracy and performance to prevent inadvertent consequences.

CURA: Real Time Artificial Intelligence and IoT based Fall Detection Systems for patients suffering from Dementia

According to the rising concern of the effects on the families due to dementia suffering patients, we aim to provide caretakers a work-life balance in which monitoring can be done with much more ease and efficiency in real time. This device can also be used in old age homes as well as hospitals which reduces the workload of the caretakers and helps them to easily monitor the patients. We aim to contribute for the betterment of the society and provide a virtual assistance for the patients suffering from dementia. The number of elderly people living alone has been increasing all over the world. If dementia has been detected at an early stage, the progress of disease can be slowed. The patients suffering from dementia are prone to falling quite frequently so as to detect that and to alert their caretakers to take necessary actions. In this study, we proposed a system in which we detect the real time state of the elderly people living alone by using the Machine Learning and IoT (Internet of Things) technology.We installed sensors inside a finger strap which is attached to the person. These sensors can detect the motions of the patient and predict their real time state to have a 24 by 7 support to provide assistance to the patients.

A framework for proactive safety evaluation of intersection using surrogate safety measures and non-compliance behavior

In recent years, identifying road users' behavior and conflicts at intersections have become an essential data source for evaluating traffic safety. According to the Federal Highway Administration (FHWA), in 2020, more than 50% of fatal and injury crashes occurred at or near intersections, necessitating further investigation. This study developed an innovative artificial intelligence (AI)-based video analytic tool to assess intersection safety using surrogate safety measures and non-compliance behavior. To extract the trajectory data, a real-time AI detection model - YOLO-v5 with a tracking framework based on the DeepSORT algorithm was deployed. 54 h of high-resolution video data were collected at six signalized intersections (including three 3-leg and three 4-leg intersections) in Glassboro, New Jersey. Non-compliance behaviors, such as redlight running and pedestrian crossing outside the crosswalk, are captured to better understand the risky behaviors at these locations. The proposed approach achieved an accuracy of 92% to 98% for detecting and tracking road users' trajectories. Additionally, the developed tool also provided directional traffic volumes, pedestrian volumes, vehicles running a red light, pedestrian crossing outside the crosswalk events, and PET and TTC for crossing conflicts between vehicles. Furthermore, an extreme value theory (EVT) was used to estimate the number of crashes at each intersection utilizing the frequency of PETs and TTCs. Finally, the intersections were ranked based on the calculated score considering the severity of crashes. Overall, the developed tool and the crash estimation, as well as the model and ranking method, can provide valuable information for engineers and policymakers to assess the safety of intersections and implement effective countermeasures to mitigate intersection-involved crashes.

Real-time Artificial Intelligence Navigation-Assisted Anatomical Recognition in Laparoscopic Colorectal Surgery

Real-time Artificial Intelligence Navigation-Assisted Anatomical Recognition in Laparoscopic Colorectal Surgery