Multiple Experts Research Articles

Fully Automated Region-Specific Human-Perceptive-Equivalent Image Quality Assessment: Application to 18F-FDG PET Scans

Introduction We propose a fully automated framework to conduct a region-wise image quality assessment (IQA) on whole-body 18F-FDG PET scans. This framework (1) can be valuable in daily clinical image acquisition procedures to instantly recognize low-quality scans for potential rescanning and/or image reconstruction, and (2) can make a significant impact in dataset collection for the development of artificial intelligence–driven 18F-FDG PET analysis models by rejecting low-quality images and those presenting with artifacts, toward building clean datasets. Patients and Methods Two experienced nuclear medicine physicians separately evaluated the quality of 174 18F-FDG PET images from 87 patients, for each body region, based on a 5-point Likert scale. The body regisons included the following: (1) the head and neck, including the brain, (2) the chest, (3) the chest-abdomen interval (diaphragmatic region), (4) the abdomen, and (5) the pelvis. Intrareader and interreader reproducibility of the quality scores were calculated using 39 randomly selected scans from the dataset. Utilizing a binarized classification, images were dichotomized into low-quality versus high-quality for physician quality scores ≤3 versus >3, respectively. Inputting the 18F-FDG PET/CT scans, our proposed fully automated framework applies 2 deep learning (DL) models on CT images to perform region identification and whole-body contour extraction (excluding extremities), then classifies PET regions as low and high quality. For classification, 2 mainstream artificial intelligence–driven approaches, including machine learning (ML) from radiomic features and DL, were investigated. All models were trained and evaluated on scores attributed by each physician, and the average of the scores reported. DL and radiomics-ML models were evaluated on the same test dataset. The performance evaluation was carried out on the same test dataset for radiomics-ML and DL models using the area under the curve, accuracy, sensitivity, and specificity and compared using the Delong test with P values <0.05 regarded as statistically significant. Results In the head and neck, chest, chest-abdomen interval, abdomen, and pelvis regions, the best models achieved area under the curve, accuracy, sensitivity, and specificity of [0.97, 0.95, 0.96, and 0.95], [0.85, 0.82, 0.87, and 0.76], [0.83, 0.76, 0.68, and 0.80], [0.73, 0.72, 0.64, and 0.77], and [0.72, 0.68, 0.70, and 0.67], respectively. In all regions, models revealed highest performance, when developed on the quality scores with higher intrareader reproducibility. Comparison of DL and radiomics-ML models did not show any statistically significant differences, though DL models showed overall improved trends. Conclusions We developed a fully automated and human-perceptive equivalent model to conduct region-wise IQA over 18F-FDG PET images. Our analysis emphasizes the necessity of developing separate models for body regions and performing data annotation based on multiple experts’ consensus in IQA studies.

Do Financial Community’s Tweet Sentiments Affect the Stock Market? Evidence from an Emerging Economy

In the era of social media and internet, Twitter (presently ‘X’) has been recognized as the prominent factor for financial markets by multiple corporates and experts. The motivation of present research hinges on burgeoning popularity of Twitter in the financial community for sharing information and impact of their hidden sentiments on financial markets. Research on tweet-based sentiments and their influence on financial markets in India is still in the infancy stage. To cover this gap, the present study empirically investigates the predictive impact of financial tweet sentiments on the Indian stock Index’s returns over a short period. The paper scraped 22,816 tweets and Nifty 50 index’s intraday hour end values for January 2023. Using Natural Processing Technique, the overall sentiments of the financial community are found as highly positive for each trading hour. Next, it presents a low positive correlation between positive sentiments, sentiment polarity, and hourly returns. Lagged negative sentiments reflect the better ability to impact current period returns than positive sentiments and polarity. Additionally, sentiment polarity and returns reflect bi-directional causation for the future period impact. Investors, stock market traders, and other practitioners can employ the proven concept of linkage between tweet sentiments and returns for constructing effective investment and trading strategies. JEL Classifications: G41, D53, G17, G02, G19

A Machine Learning Framework for Assessing Experts’ Decision Quality

Expert workers make non-trivial decisions with significant implications. Experts’ decision accuracy is, thus, a fundamental aspect of their judgment quality, key to both management and consumers of experts’ services. Yet, in many important settings, transparency in experts’ decision quality is rarely possible because ground truth data for evaluating the experts’ decisions is costly and available only for a limited set of decisions. Furthermore, different experts typically handle exclusive sets of decisions, and thus, prior solutions that rely on the aggregation of multiple experts’ decisions for the same instance are inapplicable. We first formulate the problem of estimating experts’ decision accuracy in this setting and then develop a machine–learning–based framework to address it. Our method effectively leverages both abundant historical data on workers’ past decisions and scarce decision instances with ground truth labels. Using both semi-synthetic data based on publicly available data sets and purposefully compiled data sets on real workers’ decisions, we conduct extensive empirical evaluations of our method’s performance relative to alternatives. The results show that our approach is superior to existing alternatives across diverse settings, including settings that involve different data domains, experts’ qualities, and amounts of ground truth data. To our knowledge, this paper is the first to posit and address the problem of estimating experts’ decision accuracies from historical data with scarce ground truth, and it is the first to offer comprehensive results for this problem setting, establishing the performances that can be achieved across settings as well as the state-of-the-art performance on which future work can build. This paper was accepted by Anindya Ghose, information systems. Funding: T. Geva acknowledges research grants from the Jeremy Coller Foundation and from the Henry Crown Institute for Business Research. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03357 .

Discoveries, nations, and archaeological explorations: The scientific colonization of Tarapacá, Atacama desert

The discovery of the “ruins” of Guatacondo, in the Atacama Desert of northern Chile, made the news in 1963. It was the pinnacle of a process of scientific colonization of this territory that had begun in the 19th century and connected multiple experts with the desert and its history. This paper explores the context of discovery of an archaeological site in the Atacama to show how archaeology—as a field of knowledge and a scientific practice—emerged in the interstices of projects of colonial expansion and capitalist modernization, often merging public and private interests. Weaving together histories of scientific explorations, museum operations, and extractivist enterprises, it highlights deep connections between geopolitics and ideology with the seemingly apolitical and objective nature of archaeology. Starting with the peripheral figure of Emil de Bruyne—an engineer employed by one of the biggest mining operations in the Atacama Desert of northern Chile—we explore the role of different actors within a network of well-connected people whose personal histories, intellectual backgrounds, and personal positions gave shape to the practice of archaeology in this part of the Atacama Desert.

Preserving text space integrity for robust compositional zero-shot learning via mixture of pretrained experts

In the current landscape of Compositional Zero-Shot Learning (CZSL) methods that leverage CLIP, the predominant approach is based on prompt learning paradigms. These methods encounter significant computational complexity when dealing with a large number of categories. Additionally, when confronted with new classification tasks, there is a necessity to learn the prompts again, which can be both time-consuming and resource-intensive. To address these challenges, We present a new methodology, named the Mixture of Pretrained Expert (MoPE), for enhancing Compositional Zero-shot Learning through Logit-Level Fusion with Mutile Expert Fusion Module. The MoPE skillfully blends the benefits of extensive pre-trained models like CLIP, Bert, GPT-3 and Word2Vec for effectively tackling Compositional Zero-shot Learning. Firstly, we extract the text label space for each language model individually, then map the visual feature vectors to their respective text spaces. This maintains the integrity and structure of the original text space. During this process, the pre-trained expert parameters are kept frozen. The mapping of visual features to the corresponding text spaces is subject to learning and could be considered as multiple learnable visual experts. In the model fusion phase, we propose a new fusion strategy that features a gating mechanism that adjusts the contributions of various models dynamically. This enables our approach to adapt more effectively to a range of tasks and data sets. The method’s robustness is demonstrated by the fact that the language model is not tailored to specific downstream task datasets or losses. This preserves the larger model’s topology and expands the potential for application. Preliminary experiments conducted on the UT-Zappos, AO-Clever, and C-GQA datasets indicate that MoPE performs competitively when compared to existing techniques.

Learning a generalizable re-identification model from unlabelled data with domain-agnostic expert

In response to real-world scenarios, the domain generalization (DG) problem has spurred considerable research in person re-identification (ReID). This challenge arises when the target domain, which is significantly different from the source domains, remains unknown. However, the performance of current DG ReID relies heavily on labor-intensive source domain annotations. Considering the potential of unlabeled data, we investigate unsupervised domain generalization (UDG) in ReID. Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain. To address this, we propose a new approach that trains a domain-agnostic expert (DaE) for unsupervised domain-generalizable person ReID. This involves independently training multiple experts to account for label space inconsistencies between source domains. At the same time, the DaE captures domain-generalizable information for testing. Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting. The results demonstrate the superiority of our method over state-of-the-art techniques. We will make our code and models available for public use.

Dynamic collaborative learning with heterogeneous knowledge transfer for long-tailed visual recognition

Solving the long-tailed visual recognition with deep convolutional neural networks is still a challenging task. As a mainstream method, multi-experts models achieve SOTA accurary for tackling this problem, but the uncertainty in network learning and the complexity in fusion inference constrain the performance and practicality of the multi-experts models. To remedy this, we propose a novel dynamic collaborative learning with heterogeneous knowledge transfer model (DCHKT) in this paper, in which experts with different expertise collaborate to make predictions. DCHKT consists of two core components: dynamic adaptive weight adjustment and heterogeneous knowledge transfer learning. First, the dynamic adaptive weight adjustment is designed to shift the focus of model training between the global expert and domain experts via dynamic adaptive weight. By modulating the trade-off between the learning of features and classifier, the dynamic adaptive weight adjustment can enhance the discriminative ability of each expert and alleviate the uncertainty of model learning. Then, heterogeneous knowledge transfer learning, which measures the distribution differences between the fusion logits of multiple experts and the predicted logits of each expert with different specialties, can achieve message passing between experts and enhance the consistency of ensemble prediction in model training and inference to promote their collaborations. Finally, extensive experimental results on public long-tailed datasets: CIFAR-LT, ImageNet-LT, Place-LT and iNaturalist2018, demonstrate the effectiveness and superiority of our DCHKT.

Party system ideological polarization in Western Europe: data, trends, drivers, and links with other key party system properties (1945–2021)

ABSTRACT Unlike other key party system properties (e.g. fragmentation, volatility), polarization lacks a publicly available cross-national and longitudinal dataset that can be used as an established source among the scientific community. This article aims to fill this gap by introducing a novel publicly available dataset of party system ideological polarization in Western European party systems. The dataset relies on multiple expert surveys and provides measures of party system ideological polarization for each parliamentary election and legislature in 20 Western European countries since 1945. The article also presents party system polarization’s comparative longitudinal trends, drivers, and links with other key party system properties. It finds that party system ideological polarization is on the rise in Western Europe, mainly due to a progressive shift in the electoral support from ideologically moderate mainstream parties to more extreme challenger parties. Moreover, in recent years, high party system polarization has recurred in highly fragmented and volatile contexts, thus creating a detrimental context for the working of democracy.

Frequent Use of Baby Food Pouches in Infants and Young Children and Associations with Energy Intake and BMI: An Observational Study.

Most wet commercial infant foods are now sold in squeezable 'pouches'. While multiple expert groups have expressed concern about their use, it is not known how commonly they are consumed and whether they impact energy intake or body mass index (BMI). The objectives were to describe pouch use, and determine associations with energy intake and BMI, in infants and young children. In this observational cross-sectional study of 933 young New Zealand children (6.0 months-3.9 years), pouch use was assessed by a questionnaire ('frequent' use was consuming food from a baby food pouch ≥5 times/week in the past month), usual energy intake using two 24-h recalls, and BMI z-score calculated using World Health Organization standards. The sample broadly represented the wider population (27.1% high socioeconomic deprivation, 22.5% Māori). Frequent pouch use declined with age (infants 27%, toddlers 16%, preschoolers 8%). Few children were both frequent pouch users and regularly used the nozzle (infants 5%, toddlers 13%, preschoolers 8%). Preschoolers who were frequent pouch users consumed significantly less energy than non-users (-580 kJ [-1094, -67]), but infants (115 [-35, 265]) and toddlers (-206 [-789, 378]) did not appear to have a different energy intake than non-users. There were no statistically significant differences in the BMI z-score by pouch use. These results do not support the strong concerns expressed about their use, particularly given the lack of evidence for higher energy intake or BMI.

The effect of cumulative eye movements’ guidance of experts on transition from novice to expert

AbstractBased on the assumptions of cognitive load theory, this study aims to utilize the eye movement data collected from multiple experts to scaffold novice graphic designers. The study has two main stages. In the first stage, eye tracking was used to record the eye movements of 7 experts, who covered eight topics explaining how to use Photoshop. The areas of interest and fixation durations were analyzed for each topic to extract the common patterns. Instructional videos were produced with the guidance of both eye movement patterns and multimedia design principles. In the second stage of this study, those videos were delivered to 30 university students. The experiment group watched the guided videos, whereas the control group watched straight videos, i.e., content without guidance. The guidance elements were reduced (faded) as students got familiar with the interface. All participants were assigned to perform tasks as soon as they completed the videos. The findings showed that the knowledge scores did not significantly differ between groups. Still, the average performance scores of the experiment group were considerably higher than that of the control group. Considering the transition process, the experiment group outperformed the others. The topics in which experts’ eye movements highly matched each other resulted in high-performance gaps among groups.

Determinantal Point Processes Guided Crowd-wise Mixture-of-Experts for Recommendation in Alipay

Facing the challenges of sparsity and long tail in thousands of Mini-apps recommendation scenarios deployed on Alipay platform, there is a great need for a simple, effective and easy-to-deploy industrial solution. To address this issue, we follow the strategy of ‘divide and conquer’ and propose a crowd-based recommendation model by using D eterminantal P oint P rocesse s on C rowd-wise M ixture- o f- E xperts (DPPs-CMoE). Specifically, under the guidance of DPPs-based prototypical tags, the user profiling space is sequentially divided into multiple crowds, with each of them taking on a unique latent specificity; Meanwhile, by treating the modeling of crowd specificity as one of multiple tasks, a crowd-wise architecture is adopted to seamlessly unify the multiple expert networks from the overall user space and the gating network from each of independent crowd spaces. The effectiveness of the proposed method has been illustrated in the experimental results on a mini-apps recommendation scenario deployed in Alipay APPs.

Assessment of emotion knowledge in a bilingual context and its relationship with adaptive behavior in early childhood education

Multiple education organizations and experts, including the recent education law in Spain, emphasize the significance of nurturing emotional skills from childhood to prevent the development of emotional and behavioral disorders. One of these skills is emotion knowledge (EK), which is closely linked to personal and social adjustment from early years, and later psychosocial adaptation. The present study aims to provide an assessment tool for EK in the bilingual context of the Basque Country and to provide evidence about the relationship between EK and various behavioral outcomes in early childhood education. The research involved 455 children between 3 and 6 years of old. The Emotion Matching Task, which measures the EK, was adapted to the bilingual context of the Basque Country has shown good psychometric properties and appropriate factor fit. The study also revealed the factor invariance of the Spanish and Basque versions, as well as invariance by sex. The results demonstrated that girls have higher EK than boys. Additionally, EK was associated with fewer externalizing, internalizing, and attention problems, as well as with lower atypicality and withdrawal, while higher EK was linked to greater adaptive skills. These findings support the need for interventions focused on the development of emotional skills and the suitable tools to assess their effectiveness.

Expert management consensus for the application of point-of-care testing in emergency and critical care (2024)

Point-of-care testing (POCT) is a testing approach that can be completed promptly, and for patients with acute and severe diseases, it can help clinicians identify changes in patients' conditions early and save patients' lives. Currently, the application of POCT in critical and emergency situations lacks a comprehensive clinical management consensus, leading to a lack of standardized guidance for its practical application. In order to standardize the application of POCT in critical and emergency situations, Emergency Medicine Association of Wuhan Medical Association organized multiple experts in the field of emergency and critical care to discuss and identify the current clinical issues that require standardized consensus in the application of POCT in emergency and critical care. Furthermore, a comprehensive literature search of domestic and international literature was conducted, and discussions and modifications were organized among emergency and critical care medicine experts. Ultimately, the Expert management consensus for the application of point-of-care testing in emergency and critical care (2024), which involves personnel management, equipment management and data management was formed. This consensus aims to provide more comprehensive standards and guidance for the application of POCT in critical care, thereby improving the quality of patient care and medical efficiency.

Analisis Nilai-Nilai Pendidikan Pada Novel A+ Karya Ananda Putri Sebagai Bahan Ajar Pembelajaran Sastra Di SMA

The significance of education is a crucial value that is both instructive and practical for one's lifetime. Teachers in school or educators can transmit these ideals to their students. Presently, there is a prevailing belief that the significance of education should be given precedence in response to the declining moral and ethical growth of pupils. There is a significant prevalence of adolescent delinquency among pupils, including behaviors such as bullying, physical altercations, promiscuity, and others. The significance of education in daily life can be elucidated by examining literary works, particularly novels. The purpose of this study was to analyze the educational values present in the novel "A+" by Ananda Putri and to assess the potential use of these values as teaching resources for literary instruction in high schools. The research methodology employed is a qualitative approach utilizing reading techniques and note-taking methodologies. The research findings indicate that the novel A+ by Ananda Putri encompasses 14 educational values, specifically: 1) religious, 2) honest, 3) tolerance, 4) discipline, 5) hard work, 6) democratic, 7) curiosity, 8) respect for achievement, 9) friendly/communicative, 10) love of peace, 11) fondness for reading, 12) social care, 13) responsibility, and 14) independence. Moreover, these findings were utilized as alternative instructional resources in the form of educational value-based learning modules for twelfth-grade high school students. After undergoing validation by multiple experts, this training material achieved a score of 79.88%.

Performance evaluation of a hyphenated laser spectroscopy system with conventional methods for microplastic analysis

Microplastics are one of the concerning environmental pollutants because of their ubiquity. Their capability to adsorb other environmental pollutants increases the risk even further. Existing identification approaches for microplastic characterization for polymer class and their surface-adsorbed heavy metal detection require the utilization of multiple resources and expertise. The article discusses the applicability of a custom-made hyphenated Laser Induced Breakdown Spectroscopy (LIBS)—Raman spectroscopic system in characterizing microplastics by comparing the analytical performance with conventional methods such as Attenuated Total Reflectance- Fourier Transform Infrared (ATR-FTIR) spectroscopy, confocal Raman spectroscopy, and Scanning Electron Microscopy–Energy Dispersive X-ray Spectroscopy (SEM–EDS). Raman analysis identified polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) plastics, which is confirmed by confocal Raman and FTIR study of the same. LIBS study of microplastics detected heavy metals such as Al, Ni, Co, and Zn, along with Ca and Mg trace elements. The cross-examination with EDS validates these trace elements' presence on the microplastics' surface. The results of the reported LIBS-Raman analysis and its validity evaluated using conventional gold-standard methods show the applicability of the proposed methodology in characterizing microplastics from environmental resources with less or no sample preparation in short time.

Assessing ChatGPT's Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study.

ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. The aim of this study was to evaluate ChatGPT's competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. We developed a model termed PanelGPT to assess ChatGPT's competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education.

Optimization model for fair judging of large-scale innovation contests based on experts’ neutral weights

The group decision-making method and standard score method are commonly used for evaluating large-scale innovative contest entries. However, the standard score method assumes that the quality distribution of entries evaluated by each expert remains consistent, which is difficult to achieve in practice due to different experts reviewing different entries. To enhance fairness in judging results for such contests, we propose an improved standard score method based on weighting. First, we establish a mathematical model for cross-assignment of entries and design an algorithm to solve it while discussing one property on its optimal solution. This model enables any two experts to be assigned as many identical entries as possible, facilitating reliable pairwise comparisons matrix while avoiding assigning the best or worst entries to multiple experts in one judgment. Second, inspired by cross entropy concepts, we introduce a definition of “cross entropy-like” and establish a mathematical model using this concept along with minimum sum of squared errors method to determine neutral weights for experts. Finally, experiments demonstrate that our proposed improved standard score method is more scientific and reasonable than traditional standard score method while enhancing objectivity and fairness of evaluation results.

Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters

In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. However, when data is annotated by multiple experts, standard deep learning models are often not applicable. In this paper, we propose a novel neural network framework called Multi-rater Prism (MrPrism) to learn medical image segmentation from multiple labels. Inspired by iterative half-quadratic optimization, MrPrism combines the task of assigning multi-rater confidences and calibrated segmentation in a recurrent manner. During this process, MrPrism learns inter-observer variability while taking into account the image’s semantic properties and finally converges to a self-calibrated segmentation result reflecting inter-observer agreement. Specifically, we propose Converging Prism (ConP) and Diverging Prism (DivP) to iteratively process the two tasks. ConP learns calibrated segmentation based on multi-rater confidence maps estimated by DivP, and DivP generates multi-rater confidence maps based on segmentation masks estimated by ConP. Experimental results show that the two tasks can mutually improve each other through this recurrent process. The final converged segmentation result of MrPrism outperforms state-of-the-art (SOTA) methods for a wide range of medical image segmentation tasks. The code is available at https://github.com/WuJunde/MrPrism.

Distilling consistent relations for multi-source domain adaptive person re-identification

Although unsupervised person re-identification has made great progress by employing domain adaptation, existing methods rarely make use of multiple source datasets. These works neglect a practical scenario where annotated data from multiple domains are available. To address this issue, we propose a novel method to exploit the diversity and consistency of multiple sources to improve domain adaptation for person re-identification. In the proposed method, multiple expert models pre-trained in different source domains are adapted to the target domain by self-training with expert-specific clustering-based pseudo labels. To improve the consistency of multiple experts, we transfer knowledge between experts through dual similarity distillation between experts. Finally, to further encourage experts to learn complementary features, we maintain the feature diversity of multiple experts by representation decorrelation, preventing them from being too similar. Extensive experiments conducted on three benchmarks, Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of the proposed method.

Role of MRI in classification and preoperative evaluation of perianal fistula

BackgroundA fistula is an unusual communication between the skin and an inward organ or between the organs. The current study aimed to determine the role of magnetic resonance imaging (MRI) in the diagnosis, description, and classification of fistula-in-ano. Additionally, the study aimed to compare the MRI findings with the operative findings, which is considered the gold standard in our study.MethodsThe present study is a prospective study and was carried out on a group of 61 patients: 52 males and 9 females with a mean age of 41 years, who were suspected to have perianal fistulae presented clinically with discharge or localized pain or were asymptomatic. A preoperative MRI was conducted for fistula-in-ano evaluation in these patients from October 2019 till the end of October 2021. A surgeon conducted a physical examination of all patients to document the number and location of cutaneous openings following a full medical history. The MRI images were evaluated and interpreted by multiple expert radiologists who had more than 5 years of experience in analyzing MRI. In the cases (in 3 cases, 4.92%) where there were discrepancies in their interpretations, a senior radiologist’s evaluation was considered the final result and was confirmed surgically. Subsequently, the MRI findings were correlated with surgical findings to indicate the specificity, sensitivity, and accuracy of such MRI findings.ResultsThe MRI technique was the routine (i.e., without contrast administration) technique in 36 (59.02%) patients, with contrast administration in 21 (34.43%) patients and with anesthesia/sedation (uncomfortable, anxious, and claustrophobic patients need to minimize their motion artifact to improve the quality and obtain more detailed images) in 4 (6.56%) patients. The MRI pathology of the studied patients revealed normal findings in 1 (1.64%) patient, anal fissure/early developing tract in 6 (9.84%) patients, and established tract (sinus/fistula) in 54 (88.52%) patients. Park’s and St. James's University Hospital classifications were utilized to categorize the patients, with the most common fistula types based on the Park’s classification: intersphincteric (44.3%) and transsphincteric (29.5%). Based on the St. James’s classification, grade 1 (intersphincteric (34.4%)) and grade 4 (transsphincteric with abscess/side branch (16.4%)) were the most prevalent. A substantial consensus was reached between MRI and surgery findings for classifying tracts, side branches, and abscesses formation with sensitivity, specificity, and accuracy of 100%, 85.71%, and 98.36%, respectively.ConclusionsMRI is a valuable tool in managing patients with perianal fistulas due to its ability to detect hidden areas of infection (such as abscesses) and secondary extensions. These factors contribute to the high recurrence rate after surgery. Additionally, MR imaging can define the anatomical relationships between the fistula and anal sphincters, helping to predict the likelihood of fecal incontinence following surgery.