Proposed Artificial Intelligence System Research Articles

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas

The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.

Interpretable artificial intelligence-assisted embryo selection improved single-blastocyst transfer outcomes: a prospective cohort study

Research questionWhat is the pregnancy and neonatal outcomes of an interpretable artificial intelligence (AI) model for embryo selection in a prospective clinical trial? DesignThis single-centre prospective cohort study was carried out from October 2021 to March 2022. A total of 330 eligible patients were assigned to their preferred groups, with 250 patients undergoing a fresh single-blastocyst transfer cycle after the exclusion criteria had been applied. For the AI-assisted group (AAG), embryologists selected the embryos for transfer based on the ranking recommendations provided by an interpretable AI system, while with the manual group, embryologists used the Gardner grading system to make their decisions. ResultsThe implantation rate was significantly higher in the AAG than the manual group (80.87% versus 68.15%, P = 0.022). No significant difference was found in terms of monozygotic twin rate, miscarriage rate, live birth rate and ectopic pregnancy rate between the groups. Furthermore, there was no significant difference in terms of neonatal outcomes, including gestational weeks, premature birth rate, birth height, birthweight, sex ratio at birth and newborn malformation rate. The consensus rate between the AI and retrospective analysis by the embryologists was significantly higher for good-quality embryos (i.e. grade 4BB or higher) versus poor-quality embryos (i.e. less than 4BB) (84.71% versus 25%, P < 0.001). ConclusionsThese prospective trial results suggest that the proposed AI system could effectively help embryologists to improve the implantation rate with single-blastocyst transfer compared with traditional manual evaluation methods.

Barriers and Enablers for Implementation of an Artificial Intelligence-Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews.

Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system. Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework. Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized. Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video)

Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI. Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated. The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents. The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

A Multimodal Multipath Artificial Intelligence System for Diagnosing Gastric Protruded Lesions on Endoscopy and Endoscopic Ultrasonography Images.

The aim of this study was to develop a novel artificial intelligence (AI) system that can automatically detect and classify protruded gastric lesions and help address the challenges of diagnostic accuracy and inter-reader variability encountered in routine diagnostic workflow. We analyzed data from 1,366 participants who underwent gastroscopy at Jiangsu Provincial People's Hospital and Yangzhou First People's Hospital between December 2010 and December 2020. These patients were diagnosed with submucosal tumors (SMTs) including gastric stromal tumors (GISTs), gastric leiomyomas (GILs), and gastric ectopic pancreas (GEP). We trained and validated a multimodal, multipath AI system (MMP-AI) using the data set. We assessed the diagnostic performance of the proposed AI system using the area under the receiver-operating characteristic curve (AUC) and compared its performance with that of endoscopists with more than 5 years of experience in endoscopic diagnosis. In the ternary classification task among subtypes of SMTs using modality images, MMP-AI achieved the highest AUCs of 0.896, 0.890, and 0.999 for classifying GIST, GIL, and GEP, respectively. The performance of the model was verified using both external and internal longitudinal data sets. Compared with endoscopists, MMP-AI achieved higher recognition accuracy for SMTs. We developed a system called MMP-AI to identify protruding benign gastric lesions. This system can be used not only for white-light endoscope image recognition but also for endoscopic ultrasonography image analysis.

Artificialintelligence‐basedremote monitoring system for automated anomaly detection in wireless sensor networks

SummaryIn recent decades, wireless sensor networks (WSNs) are employed in the remote monitoring system which experiences a huge delay due to the excessive time consumption by the image processing techniques. The existing video surveillance system is unable to detect the anomaly accurately and falls short in classifying the routine activity and the anomaly activities. Therefore, an efficient anomaly detection model is necessitated for identifying the anomaly behavior of malicious nodes in the WSN environment. This research work introduces a novel artificial intelligence (AI)‐based remote monitoring system for WSN. Primarily, the online video frames are retrieved from the wireless video sensors where the frames are generated by segmenting the entire stream of video as a constant‐sized structural frame. The sequence/stream of video segments is fed into the proposed AI framework to detect anomaly activities. It has multiple levels of hidden layers to compare the features of the input captured image with the preloaded reference image. Furthermore, a 5‐fold cross‐validation approach has been inspected to validate the efficacy of the suggested AI model. The performance results evident that the proposed AI system acquires a better prediction accuracy of 98.2% and precision of 97.3% than the existing monitoring systems.

Artificial intelligence for diagnosing gastric lesions under white-light endoscopy.

The ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study aimed to develop an artificial intelligence (AI) system that could diagnose six common gastric lesions under WLE and to explore its role in assisting endoscopists in diagnosis. Images of early gastric cancer, advanced gastric cancer, submucosal tumor, polyp, peptic ulcer, erosion, and lesion-free gastric mucosa were retrospectively collected to train and test the system. The performance of the system was compared with that of 12 endoscopists. The performance of endoscopists with or without referring to the system was also evaluated. A total of 29,809 images from 8947 patients and 1579 images from 496 patients were used to train and test the system, respectively. For per-lesion analysis, the overall accuracy of the system was 85.7%, which was comparable to that of senior endoscopists (85.1%, P = 0.729) and significantly higher than that of junior endoscopists (78.8%, P < 0.001). With system assistance, the overall accuracies of senior and junior endoscopists increased to 89.3% (4.2%, P < 0.001) and 86.2% (7.4%, P < 0.001), respectively. Senior and junior endoscopists achieved varying degrees of improvement in the diagnostic performance of other types of lesions except for polyp. The diagnostic times of senior (3.8 vs 3.2s per image, P = 0.500) and junior endoscopists (6.2 vs 4.6s per image, P = 0.144) assisted by the system were both slightly shortened, despite no significant differences. The proposed AI system could be applied as an auxiliary tool to reduce the workload of endoscopists and improve the diagnostic accuracy of gastric lesions.

Application of artificial intelligence (AI) control system on chiller plant at MTR station

Chillers account for up to 40% of total station energy consumption in the Hong Kong Mass Transit Railway (MTR) system. As part of green railway initiatives, a site trial was conducted to apply a fully automated AI system to control a chiller plant in order to optimise energy performance in real time while maintaining a level of passenger comfort that suits each station’s environment. Through the predictive power of the AI system, the plant power’s consumption and cooling demands can be forecasted based on actual chiller, station, and weather conditions, all of which vary over time. The optimal operational settings can then be determined using an optimisation model for real-time chiller plant control, including staging, sequencing, chilled water supply temperature set-point, etc. This paper presents the formulation of an AI system using data-driven machine learning models and numerical optimisation, and the comparison of the actual energy performance of the proposed system against rule-based control optimisation in a conventional building management system (BMS) through the site trial. The results revealed the proposed AI system achieves better energy efficiency with annual energy savings of approximately 8.7%.

Artificial intelligence for diagnosing microvessels of precancerous lesions and superficial esophageal squamous cell carcinomas: a multicenter study

BackgroundIntrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC.MethodsImages of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists.ResultsA total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated.ConclusionsThe proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.

Smart Surveillance System Real-Time Multi-Person Multi-Camera Tracking at the Edge

Abstract: Nowadays, new Artificial Intelligence (AI) and Deep Learning based processing methods are replacing traditional computer vision algorithms. On the other hand, the rise of the Internet of Things (IoT) and edge computing, has led to many research works that propose distributed video-surveillance systems based on this notion. Usually, the advanced systems process massive volumes of data in different computing facilities. Instead, this paper presents a system that incorporates AI algorithms into low-power embedded devices. The computer vision technique, which is commonly used in surveillance applications, is designed to identify, count, and monitor people's movements in the area. A distributed camera system is required for this application. The proposed AI system detects people in the monitored area using a MobileNet-SSD architecture. This algorithm can keep track of people in the surveillance providing the number of people present in the frame. The proposed framework is both privacy-aware and scalable supporting a processing pipeline on the edge consisting of person detection, tracking and robust person re-identification. The expected results show the usefulness of deploying this smart camera node throughout a distributed surveillance system. Keywords: Edge Analytics, Person detection, Person re-identification, Deep learning, embedded systems, artificial intelligence

Detection of maxillary sinus fungal ball via 3-D CNN-based artificial intelligence: Fully automated system and clinical validation.

BackgroundThis study aims to develop artificial intelligence (AI) system to automatically classify patients with maxillary sinus fungal ball (MFB), chronic rhinosinusitis (CRS), and healthy controls (HCs).MethodsWe collected 512 coronal image sets from ostiomeatal unit computed tomography (OMU CT) performed on subjects who visited a single tertiary hospital. These data included 254 MFB, 128 CRS, and 130 HC subjects and were used for training the proposed AI system. The AI system takes these 1024 sets of half CT images as input and classifies these as MFB, CRS, or HC. To optimize the classification performance, we adopted a 3-D convolutional neural network of ResNet 18. We also collected 64 coronal OMU CT image sets for external validation, including 26 MFB, 18 CRS, and 20 HCs from subjects from another referral hospital. Finally, the performance of the developed AI system was compared with that of the otolaryngology resident physicians.ResultsClassification performance was evaluated using internal 5-fold cross-validation (818 training and 206 internal validation data) and external validation (128 data). The area under the receiver operating characteristic over the internal 5-fold cross-validation and the external validation was 0.96 ±0.006 and 0.97 ±0.006, respectively. The accuracy of the internal 5-fold cross-validation and the external validation was 87.5 ±2.3% and 88.4 ±3.1%, respectively. As a result of performing a classification test on external validation data from six otolaryngology resident physicians, the accuracy was obtained as 84.6 ±11.3%.ConclusionsThis AI system is the first study to classify MFB, CRS, and HC using deep neural networks to the best of our knowledge. The proposed system is fully automatic but performs similarly to or better than otolaryngology resident physicians. Therefore, we believe that in regions where otolaryngology specialists are scarce, the proposed AI will perform sufficiently effective diagnosis on behalf of doctors.

Analyzing and visualizing morphological features using machine learning techniques and non‐big data: A case study of macaque mandibles

AbstractObjectivesMorphometrics has played essential roles in the comprehension of biological variation and the evolution of morphological phenotypes. This approach usually imposes strict requirements on data, such as rigid alignment of subjects, and the collection and manual preprocessing of data meeting these requirements are often time consuming. Artificial intelligence (AI) technology is developing and it potentially reduces this load, but they usually presuppose the availability of “big data” for successful learning, beyond the empirically plausible amount in biological studies. Here, we propose a deep learning‐based analysis of three‐dimensional data.Materials and MethodsWe built a deep learning‐based analysis of three‐dimensional morphological data that does not require strict alignment or an implausible sample size. We benchmarked the proposed method by case studying sex classification of macaques, referring to computed tomography scans of their mandible.ResultsThe model learned from just 139 mandible specimens of Japanese macaques and successfully generalized the learned classification to previously unseen specimens of the same species and even other species of macaques. Moreover, we visualized those characteristic regions in the data that the model used during sex classification and showed that they were consistent with the criteria used by human experts.DiscussionOur analysis does not require rigidly aligned data, so can effectively use data collected in previous studies with different focus/aims. This proposed AI method can potentially help researchers to discover new morphological features of different species and other biological groups. Implementation of this proposed AI system will be available to other researchers for further investigation.

An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P &lt; .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P &gt; .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.

Artificial intelligence-enabled rapid diagnosis of patients with COVID-19.

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

Large Scale Assessment of Consistency in Sleep Stage Scoring Rules Among Multiple Sleep Centers Using an Interpretable Machine Learning Algorithm

Background: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in the standards used by technicians. Because organizing meetings to reach a consensus among multiple sleep centers is time consuming, we developed an artificial intelligence (AI) system to evaluate the reliability and consistency of sleep scoring. Methods: An interpretable machine learning algorithm was used to evaluate interrater reliability (IRR) among sleep centers. Intra-center and inter-center assessments were conducted on 679 subjects without sleep apnea in six sleep centers in Taiwan. IRR was estimated based on prediction outcomes. Findings: In the intra-center assessment, the median accuracy of the databases ranged from 78·8% to 81·9% with the exception of one hospital (designated E) with an accuracy of 72·5%. In the inter-center assessment, the median accuracy ranged from 74·4% to 79·9% when hospital E was excluded from testing and training. The performance of the proposed method was higher for N2, awake, and REM, compared to N1 and N3. There was a significant difference in the prediction models learned from hospital E and others. Interpretation: The proposed AI system proved highly effective in assessing IRR. Increasing the interrater agreement rate would lead to further improvements in the accuracy of the proposed sleep stage annotation system. Funding: This research was supported by grants from the Ministry of Science and Technology, Taiwan (MOST-109-2119-M-002-014), and the Chang Gung Medical Research Program (CMRPG3K0201). Declaration of Interest: The authors declare no competing interests. Ethical Approval: The study protocol was approved by the Institutional Review Board of each hospital (Chang Gung Memorial Hospital’s IRB No: 201800609B0; MacKay Memorial Hospital’s IRB No: 18MMHIS142e; Shuanh-Ho Hospital’s IRB No: N201911007, N201903142; Taipei Tzu Chi Hospital’s IRB No: 07-XD-083; Taichung Tzu Chi Hospital’s IRB No: REC107-37, and Taipei Veterans General Hospital’s IRB No: 2018-12-009AC).

Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression

The prediction of asphalt performance can be very important in terms of increasing service life and performance while saving energy and money. In this study, a new hybrid artificial intelligence (AI) system, SOS–LSSVR, has been proposed to predict the permanent deformation potential of asphalt pavement mixtures. SOS–LSSVR utilizes the symbiotic organisms search (SOS) and the least squares support vector regression (LSSVR), which are seen as a complementary system. The prediction model can be established from all input and output data pairs for LSSVR, while SOS optimizes the system’s tuning parameters. To avoid sampling bias and to partition the dataset into testing and training, a cross-validation technique was chosen. The results can be compared to those of previous studies and other predictive methods. Through the use of four error indicators, SOS–LSSVR accuracy was verified in predicting the permanent deformation behavior of an asphalt mixture. The present study demonstrates that the proposed AI system is a valuable decision-making tool for road designers. Additionally, the success of SOS–LSSVR in building an accurate prediction model suggests that the proposed self-optimized prediction framework has found an underlying pattern in the current database and thus can potentially be implemented in various disciplines.