Decision-making Process Research Articles

Exploring factors influencing Koreans’ intention to visit Japan through decision tree analysis: focusing on discharge of ALPS-treated water and low-yen perception

ABSTRACT In this study, factors influencing Koreans’ intention to visit Japan were examined using a decision tree. It is known that a negative national image generally adversely affects the intention to visit tourist destinations. Japan and Korea have maintained various political, economic and historical conflicts from the past. However, Koreans accounted for the largest proportion of tourists visiting Japan. Therefore, this study aimed to explore the decision-making process for Koreans’ intention to visit Japan and analyse the priority of influencing factors using a decision tree. As a result of the analysis, low-yen perception, historical animosity, interest in treated water, cultural familiarity and age were identified as major influencing factors. These results confirm that changes in Koreans’ perceptions of Japan are progressing despite the fact that the anti-Japanese sentiment towards Japan still exists. Such changes are affecting the selection of tourist destinations. This study is significant in that it considers factors influencing international tourism demand from various perspectives by inputting various predictive variables such as politics, economy, social culture and so on.

Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Background Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditional diagnostic tests such as smear and culture tests, which can require hours to weeks to yield results. Objective This study aimed to use artificial intelligence (AI)–based chest radiography (CXR) to evaluate the infectivity of patients with PTB more quickly and accurately compared with traditional methods such as smear and culture tests. Methods We used DenseNet121 and visualization techniques such as gradient-weighted class activation mapping and local interpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXR images of 4492 patients with PTB obtained from Severance Hospital, focusing specifically on the lung region through segmentation and cropping with TransUNet. We used data from 2004 to 2020 to train the model, data from 2021 for testing, and data from 2022 to 2023 for internal validation. In addition, we used 1978 CXR images of 299 patients with PTB obtained from Yongin Severance Hospital for external validation. Results In the internal validation, the model achieved an accuracy of 73.27%, an area under the receiver operating characteristic curve of 0.79, and an area under the precision-recall curve of 0.77. In the external validation, it exhibited an accuracy of 70.29%, an area under the receiver operating characteristic curve of 0.77, and an area under the precision-recall curve of 0.8. In addition, gradient-weighted class activation mapping and local interpretable model-agnostic explanations provided insights into the decision-making process of the AI model. Conclusions This proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, with significant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings, compared with traditional smear and culture tests.

A thematic analysis of shared decision-making in consultations with patients with a presumed brain tumor and neurosurgeons

Abstract Background Patients with a brain tumor, family members and healthcare professionals face complex healthcare decisions that can significantly impact everyday life. Engaging in shared decision-making (SDM) can help ensure treatment decisions align with patients’ preferences and goals. Little is known about the decision-making process in neurosurgical consultations. This study aims to qualitatively explore and describe the SDM process during neurosurgical consultations for patients with a presumed brain tumor. Methods The first consultation between patients with a presumed glioma or meningioma and their neurosurgeons was audio-recorded and transcribed. Decision-making models were used as sensitization concepts, with coding carried out inductively. We employed a reflexive thematic analysis to develop themes and sub-themes until saturation was beginning to emerge. Results Analyzing eleven consultations, we developed three main themes and twelve sub-themes. The results depict decision-making guided by neurosurgeons (theme 1), with varying degrees of engagement from patients and family members (theme 2) and personalization of the decision-making process based on the medical situation and everyday life priorities (theme 3). The consultations lacked specific attention to everyday life priorities of the patients. Conclusions The decision-making processes seemed to focus primarily on medically informing patients with a presumed brain tumor, in line with IDM. However, less emphasis was placed on learning more about the patient’s everyday life and preferences to integrate into the decision-making process, which is key to SDM. Incorporating patient goals may lead to greater personalization and decisions that align with both the patient’s medical situation and their goals.

Critical infrastructure resilience: a guide for building indicator systems based on a multi-criteria framework with a focus on implementable actions

Abstract. Criteria and indicators are frequently used for assessing the resilience of critical infrastructure (CI). Moreover, to generate precise information on conditions, the assessment designed for CI resilience could rely on indicator systems. However, few practical tools exist for guiding CI managers to build specific indicator systems for considering real cases. Therefore, the main objective of this study is to develop a step-by-step guide that contains guidance on operational steps and required resources for criteria and indicator setting, reference definition, and data collection. This guide enables CI managers to build systems of indicators tailored to different real cases. This guide could assist CI managers in their decision-making process, as it is structured based on a multi-criteria framework that takes into account the cost–benefit analysis and side effects of implementable actions. This guide could furthermore advance the application of indicator-based CI resilience assessment in practical management. In addition, this study provides an example to demonstrate how to use this guide. This example is based on specific circumstances for the Nantes Ring Road (NRR) network: when the ring road is flooded and closed, the road network manager suggests alternative roads to the public. An indicator system consisting of 4 criteria, 7 sub-criteria, and 11 indicators was built for these circumstances using the guide developed in this paper. This example relates to criteria and indicators in the technical, social, and environmental dimensions and involves 62 676 data points.

The anatomy of clinical decision-making: aligning AI design with ICU routines

PurposeIn the face of numerous explanations for why AI-driven decision support systems (DSS) have failed to deliver on their promise of improving organizational decision-making, this paper problematizes the under-theorized mismatch between the design of DSS and the actual decision-making processes that the technology is supposed to support. We examine this mismatch by studying the implementation of a DSS in the intensive care unit (ICU) of a large academic hospital.Design/methodology/approachBased on 27 months of ethnographic fieldwork, we contend that the studied DSS was designed on the assumption that individual intensivists are responsible for making life-critical discharge decisions at one particular moment in time.FindingsHowever, our study of actual decision-making practices reveals that discharge decision-making is instead a protracted process, involving multiple actors fragmented across time and space. To account for these complexities, we advocate for a “dynamic routines” perspective, which highlights the actual patterns of action pursued throughout a clinical decision-making process.Originality/valueOur application of this perspective contributes to a more granular understanding of discharge decision-making, which can help future DSS designers better grasp the peculiarities and complexities—or “anatomy”—of the decision-making process. We also suggest integrating an “anticipatory ethnographic approach” into the design and pre-implementation phases of future DSS to help bridge the current gap between design assumptions and actual decision-making practices.

Developing Understanding of Critical Thinking Definitions, Concepts and Characteristics in TVET Higher Education

This study aims to provide an understanding of critical thinking skills in TVET higher education context by investigating the definition, concept, and characteristics of critical thinking skills that is appropriate for the context. Therefore, a data collection is done through an interview involving ten lecturers from TVET higher education institutions purposely to gather their insight, perspectives, and real practice on critical thinking skills. The interview data are analysed using thematic analysis. It was discovered that the critical thinking concept for the TVET higher education context is a decision-making process. Critical thinking is defined as reflective and reasonable thinking, and critical thinking characteristics are the ability to adapt and analyse other alternatives. From the result of thematic analysis, this study developed a conceptual framework of critical thinking skills concept, definition and characteristics for TVET higher education setting. The findings could provide a reference for TVET higher education lectures to understand and apply critical thinking skills during teaching. In addition, other researchers can use the findings to develop fundamental knowledge in exploring and researching critical thinking skills.

Influence of leadership style on employee performance at PT Pegadaian Ragional Office Region III and Palembang Branch

This study aims to analyze the influence of leadership style on employee performance at PT Pegadaian Regional Office III and Palembang Branch. Leadership style plays an important role in improving employee performance which has a direct impact on achieving company goals. This study uses a quantitative method with total sampling of all employees at PT Pegadaian Regional Office III and Palembang Branch. The results of the analysis show that leadership style has a positive and significant influence on employee performance, especially in the decision-making process, by providing employees with the opportunity to provide input. However, there are still some employees who feel less listened to, which can hinder performance improvement. The findings provide implications for the development of a more inclusive and responsive leadership strategy at PT Pegadaian.

Influence of Social Media Marketing on Destination Choice among European Travelers

Purpose: The aim of the study was to analyze the influence of social media marketing on destination choice among European travelers. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Social media marketing has a significant influence on destination choice among European travelers, as it shapes their perceptions and decision-making processes. Studies show that platforms like Instagram, Facebook, and YouTube serve as key sources of inspiration and information, with user-generated content and influencer recommendations often playing a pivotal role in shaping travel preferences. Travelers are particularly drawn to visually appealing content, which showcases destinations' attractions, culture, and experiences in an engaging manner. Unique Contribution to Theory, Practice and Policy: Uses and gratifications theory (UGT), social proof theory & elaboration likelihood Model (ELM) may be used to anchor future studies on the influence of social media marketing on destination choice among European travelers. Travel businesses should invest in creating visually compelling content that showcases their offerings. Destination marketing organizations (DMOs) should adopt policies that prioritize high-quality visual content in their marketing strategies.

Autonomous mobile robots for exploratory synthetic chemistry.

Autonomous laboratories can accelerate discoveries in chemical synthesis, but this requires automated measurements coupled with reliable decision-making1,2. Most autonomous laboratories involve bespoke automated equipment3-6, and reaction outcomes are often assessed using a single, hard-wired characterization technique7. Any decision-making algorithms8 must then operate using this narrow range of characterization data9,10. By contrast, manual experiments tend to draw on a wider range of instruments to characterize reaction products, and decisions are rarely taken based on one measurement alone. Here we show that a synthesis laboratory can be integrated into an autonomous laboratory by using mobile robots11-13 that operate equipment and make decisions in a human-like way. Our modular workflow combines mobile robots, an automated synthesis platform, a liquid chromatography-mass spectrometer and a benchtop nuclear magnetic resonance spectrometer. This allows robots to share existing laboratory equipment with human researchers without monopolizing it or requiring extensive redesign. A heuristic decision-maker processes the orthogonal measurement data, selecting successful reactions to take forward and automatically checking the reproducibility of any screening hits. We exemplify this approach in the three areas of structural diversification chemistry, supramolecular host-guest chemistry and photochemical synthesis. This strategy is particularly suited to exploratory chemistry that can yield multiple potential products, as for supramolecular assemblies, where we also extend the method to an autonomous function assay by evaluating host-guest binding properties.

Legal Perspective on the Use of Artificial Intelligence in Corporate Governance in Nigeria: Potentials and Challenges

Abstract From a legal standpoint, this paper critically examines the potential and challenges in deploying Artificial (AI) Intelligence in corporate governance in Nigeria. The examination revealed that leveraging AI in corporate governance could enhance corporate efficiency in Nigeria through improved decision-making, risk management, financial reporting, and stakeholder protection and engagement. However, possible bias and data privacy breaches are significant risks that pose ethical challenges when AI is deployed in corporate governance. Particularly, Nigeria is bisected by several socio-economic challenges, such as a lack of a robust AI framework and insufficient technological expertise to develop and optimize AI systems. Furthermore, Nigeria currently lacks comprehensive national AI legislation, thereby resulting in the absence of a legal basis for the effective deployment of AI in corporate governance and board management. Against this backdrop, this paper proposes an AI-based corporate governance framework, which can be adapted into future legislative reforms to streamline decision-making processes and improve board accountability and stakeholders’ protection in companies. Overall, it argues that AI legislation and policies are vital to the success of efforts to implement AI-based corporate governance in Nigeria.

Audit committee financial expertise and internal control weakness: does CEO overconfidence matter?

Purpose The presence of financial experts within audit committees (AC, hereafter) has been a central concern for regulatory bodies. These guidelines underscore the significance of having members with distinct financial acumen and proficiency on AC. The primary objective of this research is to explore the influence of financial expertise within AC on internal control weaknesses (ICWs, hereafter). Furthermore, another aim of this study is to examine the potential combined impact of CEO overconfidence on this correlation. Design/methodology/approach The statistical analysis has been done through the implementation of a multiple regression model by adopting a sample of 82 Jordanian companies, chosen from the years 2014 to 2020. Findings This research reveals that an AC with higher levels of financial expertise is linked to fewer ICWs. Upon further examination, this paper observes that this negative impact is primarily driven by the presence of accounting expertise, as opposed to nonaccounting expertise. It is also evident that CEOs with overconfidence may interfere with the decision-making processes of the audit committees, thus compromising the internal controls even though the committee is financially and accountancy literate. Originality/value This research distinguishes between accounting and nonaccounting financial experience in AC and shows that accounting experience significantly reduces internal control problems. In addition, it introduces CEO overconfidence as a moderating variable, which provides new insights into the field of corporate governance. The implications of these findings are far-reaching in enhancing transparency, trust and accountability by enhancing the quality of internal control.

Artificial Intelligence Techniques for Sustainable Reconfigurable Manufacturing Systems: An AI-Powered Decision-Making Application Using Large Language Models

Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes.

Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

Understanding society: a reflexive foresight matrix for navigating complexity and shaping the future

Purpose This paper introduces the foresight matrix, a conceptual framework for foresight analysis rooted in critical realism (CR) and complexity theory. It aims to address the need for a comprehensive theoretical perspective that embraces emergence and recognizes the interplay between different layers of reality. Informed by critical theory within futures studies, the study seeks to provide additional foresight theoretical perspectives in addressing contemporary societal challenges and collectively shaping desirable futures. Design/methodology/approach The proposed reflexive foresight matrix incorporates elements of CR, complexity theory and morphogenesis theory, providing an alternative conceptual framework for analyzing complex social challenges. It integrates the vertical layers – empirical, actual and real – and horizontal dimensions – structure, agency and culture. This matrix guides researchers through a systematic analysis of structural barriers, cultural influences and individual agency, offering a comprehensive tool for understanding the underlying mechanisms driving social change. Findings The reflexive foresight matrix dissects complex situations into structure, agency and culture dimensions, enabling a thorough understanding of societal dynamics by examining the interplay between these dimensions and their influence on individual and collective behaviors. This conceptual framework guides researchers through a systematic analysis of structural barriers, cultural influences and individual agency, uncovering the mechanisms driving social change. Through reflexive monitoring and temporal action analysis, it explores historical and contextual factors, informing future tendencies. Practical implications The reflexive foresight matrix provides a multidimensional lens for understanding social issues, prompting researchers to consider the interplay between structure, agency and culture across different layers of reality. By exploring historical and contextual factors, this framework enables an insight into diverse perspectives and their impact on future outlooks. Furthermore, the matrix facilitates critical inquiry into prevailing power dynamics and narratives, offering insights into societal injustices and inequalities. This critical examination empowers stakeholders to challenge dominant narratives and work toward more equitable and desirable futures. Social implications The proposed reflexive foresight matrix offers a comprehensive approach to analyzing complex social challenges, potentially impacting futures thinking and foresight practices. By integrating CR, complexity theory and morphogenesis theory, the framework fosters a deep understanding of the interplay between various factors influencing future tendencies. This comprehensive view of problems allows analysts to consider structural barriers, cultural influences and agency, thereby enhancing foresight efforts in addressing contemporary challenges. Originality/value The proposed reflexive foresight matrix contributes to futures thinking and foresight practices by offering a comprehensive approach to analyzing complex social challenges. By combining CR, complexity theory and morphogenesis theory, the framework fosters an insightful understanding of the interplay between various factors influencing future trajectories. Moreover, the integration of critical perspectives and reflexive methodologies into foresight practices underscores the importance of challenging dominant narratives, fostering inclusive decision-making processes and empowering individuals and communities to shape their futures.

What motivates SARS-CoV-2 vaccine trial participants? A pre- and post-participation survey study.

Scientific advancement, including the testing and licensing of new drugs, relies heavily on clinical trials with healthy individuals. The motivations of clinical trial participants have been discussed intensively, as some worry that financial compensation may distract from the intrinsic risk of clinical research. Herein, we investigated the motivations and decisional factors influencing SARS-CoV-2 clinical trial participants. Moreover, since most surveys are administered after clinical trial participation, we were interested in whether the results were tainted by recall bias. This was a cross-sectional observational study. Participants were administered a survey on two occasions, once before and once after participation in a clinical trial. The primary outcomes were the motivations and decisional factors of SARS-CoV-2 vaccine trial participants and the difference between the surveys collected before and after clinical trial participation. The survey response rate was 149/200 (75%). SARS-CoV-2 vaccine trial participants were mostly motivated by the desire to contribute to science and help others. Answers collected before and after the trial were not statistically different, indicating the absence of recall bias. The decision-making process of clinical trial participants is complex and multi-faceted. Previous studies have shown that clinical trial participants have mixed motivations but never to the extent reported in the current survey. Here, we present a theoretical framework that attempts to explain how different motivational factors may contribute to decision forming.

Exploring the Decision-Making Process of International Students: Factors Influencing their Choice of US Colleges

The United States is the top destination for international students seeking higher education. These students encounter various factors that shape their decisions throughout the college application journey. This research aims to pinpoint the main variables that influence international students’ choices to study in the US. Important factors include the reputation and rankings of universities, tuition costs, geographic location, personal recommendations, unique connections, and the complexity of the admissions process. Despite facing challenges like strict immigration policies and lengthy visa application procedures, US universities continue to attract students due to their global prestige and the numerous opportunities they provide for personal and professional development. The study sheds light on the preferences and considerations of international students when choosing colleges in the US. By doing so, it offers valuable insights for higher education institutions to improve their support services and refine recruitment strategies, ultimately enhancing the experiences and outcomes for international students.

Development and validation of the prediction score for augmented renal clearance in critically Ill Japanese adults.

Augmented renal clearance (ARC) decreases the therapeutic concentration of drugs excreted by the kidneys in critically ill patients. Several ARC prediction models have been developed and validated; however, their usefulness in Japan has not been comprehensively investigated. Thus, we developed a unique ARC prediction model for a Japanese mixed intensive care unit (ICU) population and compared it with existing models. This retrospective study enrolled a mixed ICU population in Japan from January 2019 and June 2022. The primary outcome was the development and validation of a model to predict ARC onset based on baseline information at ICU admission. Patients admitted until May 2021 were included in the training set, and external validation was performed on patients admitted thereafter. A multivariate logistic regression model was used to develop an integer-based predictive scoring system for ARC. The new model (the JPNARC score) was externally validated along with the ARC and Augmented Renal Clearance in Trauma Intensive Care (ARCTIC) scores. A total of 2,592 critically ill patients were enrolled initially, with 651 patients finally included after excluding 1,941 patients. The training and validation datasets comprised 456 and 195 patients, respectively. Multivariate analysis was performed to develop the JPNARC score, which incorporated age, sex, serum creatinine, and diagnosis upon ICU admission (trauma or central nervous system disease). The JPNARC score had a larger area under the receiver operating characteristic curve than the ARC and ARCTIC scores in the validation dataset (0.832, 0.633, and 0.740, respectively). An integer-based scoring system was developed to predict ARC onset in a critically ill Japanese population and showed high predictive performance. New models designed to predict the often-unrecognized ARC phenomenon may aid in the decision-making process for upward drug dosage modifications, especially in resource- and labor-limited settings.

Is Pessimism Provably Efficient for Offline Reinforcement Learning?

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a data set collected a priori. Because of the lack of further interactions with the environment, offline RL suffers from the insufficient coverage of the data set, which eludes most existing theoretical analysis. In this paper, we propose a pessimistic variant of the value iteration algorithm (PEVI), which incorporates an uncertainty quantifier as the penalty function. Such a penalty function simply flips the sign of the bonus function for promoting exploration in online RL, which makes it easily implementable and compatible with general function approximators. Without assuming the sufficient coverage of the data set (e.g., finite concentratability coefficients or uniformly lower-bounded densities of visitation measures), we establish a data-dependent upper bound on the suboptimality of PEVI for general Markov decision processes (MDPs). When specialized to linear MDPs, it matches the information-theoretic lower bound up to multiplicative factors of the dimension and horizon. In other words, pessimism is not only provably efficient but also, minimax optimal. In particular, given the data set, the learned policy serves as the “best effort” among all policies as no other policies can do better. Our theoretical analysis identifies the critical role of pessimism in eliminating a notion of spurious correlation, which arises from the “irrelevant” trajectories that are less covered by the data set and not informative for the optimal policy. Funding: Z. Yang acknowledges the Simons Institute (Theory of Reinforcement Learning). Z. Wang acknowledges the National Science Foundation [Awards 2048075, 2008827, 2015568, and 1934931], the Simons Institute (Theory of Reinforcement Learning), Amazon, J. P. Morgan, and Two bSigma.

Enhanced Curvature-Based Fabric Defect Detection: A Experimental Study with Gabor Transform and Deep Learning

Quality control at every stage of production in the textile industry is essential for maintaining competitiveness in the global market. Manual fabric defect inspections are often characterized by low precision and high time costs, in contrast to intelligent anomaly detection systems implemented in the early stages of fabric production. To achieve successful automated fabric defect identification, significant challenges must be addressed, including accurate detection, classification, and decision-making processes. Traditionally, fabric defect classification has relied on inefficient and labor-intensive human visual inspection, particularly as the variety of fabric defects continues to increase. Despite the global chip crisis and its adverse effects on supply chains, electronic hardware costs for quality control systems have become more affordable. This presents a notable advantage, as vision systems can now be easily developed with the use of high-resolution, advanced cameras. In this study, we propose a discrete curvature algorithm, integrated with the Gabor transform, which demonstrates significant success in near real-time defect classification. The primary contribution of this work is the development of a modified curvature algorithm that achieves high classification performance without the need for training. This method is particularly efficient due to its low data storage requirements and minimal processing time, making it ideal for real-time applications. Furthermore, we implemented and evaluated several other methods from the literature, including Gabor and Convolutional Neural Networks (CNNs), within a unified coding framework. Each defect type was analyzed individually, with results indicating that the proposed algorithm exhibits comparable success and robust performance relative to deep learning-based approaches.

Developing an AI-based application for caries index detection on intraoral photographs.

This study evaluates the effectiveness of an Artificial Intelligence (AI)-based smartphone application designed for decay detection on intraoral photographs, comparing its performance to that of junior dentists. Conducted at The Aga Khan University Hospital, Karachi, Pakistan, this study utilized a dataset comprising 7,465 intraoral images, including both primary and secondary dentitions. These images were meticulously annotated by two experienced dentists and further verified by senior dentists. A YOLOv5s model was trained on this dataset and integrated into a smartphone application, while a Detection Transformer was also fine-tuned for comparative purposes. Explainable AI techniques were employed to assess the AI's decision-making processes. A sample of 70 photographs was used to directly compare the application's performance with that of junior dentists. Results showed that the YOLOv5s-based smartphone application achieved a precision of 90.7%, sensitivity of 85.6%, and an F1 score of 88.0% in detecting dental decay. In contrast, junior dentists achieved 83.3% precision, 64.1% sensitivity, and an F1 score of 72.4%. The study concludes that the YOLOv5s algorithm effectively detects dental decay on intraoral photographs and performs comparably to junior dentists. This application holds potential for aiding in the evaluation of the caries index within populations, thus contributing to efforts aimed at reducing the disease burden at the community level.