Practical Tasks Research Articles

Changes of upper-limb kinematics during practice of a redundant motor task in patients with Parkinson's disease.

The ability to learn novel motor skills is essential for patients with Parkinson's disease (PD) to regain activities of daily living. However, the underlying mechanisms of motor learning in PD remain unclear. To identify motor features that are distinctively manifested in PD during motor learning, we quantified a rich set of variables reflecting various aspects of the learning process in a virtual throwing task. While the performance outcome improved similarly over 3days of practice for both PD patients and age-matched controls, further analysis revealed distinct learning processes between the two groups. PD patients initially performed with a slow release velocity and gradually increased it as practice progressed, whereas the control group began with an unnecessarily rapid release velocity, which they later stabilized at a lower value. Performance characteristics related to the timing of ball release and the inter-release interval did not show significant group differences, although they were modulated across practice in both groups. After one week, both groups retained the performance outcomes and underlying kinematics developed over practice. This study underscores the importance of analyzing the multi-faceted learning process to characterize motor skill learning in PD. The findings may provide insights into PD pathophysiology and inform rehabilitation strategies.

Needs Analysis for Designing English Course for Tourist Police in Mandalika Special Economic Zone

In the era of globalization and increasing international travel, effective communication in English has become a fundamental requirement for individuals working in various professional fields.The objectives of this research are to find out the authentic tasks and authentic texts that needed to be included in designing English for the tourist police in Mandalika Special Economic Zone (Mandalika SEZ), and to know how the design of the English course for tourist police in Mandalika Special Economic Zone (Mandalika SEZ) to improve language skills in their workplace. The subjects of the study are five tourist police officers who are fluent in English from 15 Tourist Police officers who work in the Mandalika Economic Zone. The techniques used to collect the data are observation, interview, questionnaire, and focus group discussion. The data analysis technique follows Miles and Huberman’s model (reduction, data display, conclusion drawing/verification, and syllabus design). The syllabus encompasses a total of seven key authentic tasks tailored to enhance the officers' communication and operational capabilities in various scenarios. These tasks include: Communicating During Emergencies, Giving Directions, Conducting Safety Checks, Conflict Resolution, Writing Reports, and Providing Assistance. In conclusion, the English syllabus for tourist police officers in Mandalika SEZ is carefully crafted to meet their needs through practical tasks, relevant materials, and effective assessment methods. This design enhances both language skills and real-life problem-solving abilities, serving as a valuable model for future training and improving the overall service quality and efficiency of the officers.

The performance of AI in medical examinations: an exploration of ChatGPT in ultrasound medical education

ObjectiveThis study aims to evaluate the accuracy of ChatGPT in the context of China’s Intermediate Professional Technical Qualification Examination for Ultrasound Medicine, exploring its potential role in ultrasound medical education.MethodsA total of 100 questions, comprising 70 single-choice and 30 multiple-choice questions, were selected from the examination’s question bank. These questions were categorized into four groups: basic knowledge, relevant clinical knowledge, professional knowledge, and professional practice. ChatGPT versions 3.5 and 4.0 were tested, and accuracy was measured based on the proportion of correct answers for each version.ResultsChatGPT 3.5 achieved an accuracy of 35.7% for single-choice and 30.0% for multiple-choice questions, while version 4.0 improved to 61.4 and 50.0%, respectively. Both versions performed better in basic knowledge questions but showed limitations in professional practice-related questions. Version 4.0 demonstrated significant improvements across all categories compared to version 3.5, but it still underperformed when compared to resident doctors in certain areas.ConclusionWhile ChatGPT did not meet the passing criteria for the Intermediate Professional Technical Qualification Examination in Ultrasound Medicine, its strong performance in basic medical knowledge suggests potential as a supplementary tool in medical education. However, its limitations in addressing professional practice tasks need to be addressed.

Knowledge-oriented management systems for complex objects

The article examines the issue of improving the efficiency of complex systems management through the use of knowledge-based methods and technologies. The article emphasizes the need to revise traditional approaches to scientific research and knowledge management by introducing a transdisciplinary paradigm and artificial intelligence to achieve sustainable development and knowledge evolution. The authors try to solve the problem of management in the face of uncertainty and incomplete information, which is typical for such systems. The methods of integrating knowledge from different fields to create an integrated management system are considered. The basis of management is the holarchic structure, which organizes the system in the form of a hierarchy of holons. Each holon has a specific role and knowledge, which ensures the flexibility and adaptability of the system. The study also uses self-organization and learning methods to ensure continuous improvement of the systemʼs functioning. The article discusses the requirements for knowledge-based management systems, such as flexibility, adaptability, data and knowledge integration, learning and self-organization, transparency of decisions, and resilience to errors and failures. The findings of the study emphasize the importance of implementing knowledge-based approaches in the management system of complex facilities. This allows to increase the level of adaptability and stability of management, which is critical in todayʼs environment of dynamic development of technology and knowledge. The proposed methods can be applied in various fields, which makes them universal and useful for a wide range of scientific and practical tasks, providing a high level of adaptability, efficiency and stability of complex objects management, taking into account modern requirements and challenges.

The Effects of Cognitive Remediation on Negative Symptoms in Schizophrenia-Spectrum Illness: A Meta-analytic Investigation of Efficacy.

The number of clinical efficacy trials of Cognitive Remediation (CR), a behavioral intervention consisting of cognitive task practice and/or strategy training to improve cognitive skills in schizophrenia, has increased substantially over the past 25 years. While recent reviews have highlighted the effects of CR on cognition and function, CR effects on negative symptoms remain understudied. Given the overlap between negative symptoms and cognition, CR effects might be expected. Electronic databases were evaluated up to September 2023 using a broad range of search terms. Sixty-nine unique, controlled trials that used negative symptoms as an outcome were meta-analyzed. Data were independently extracted with excellent (>98%) reliability. Random effects models assessed the effects of CR on summary and expressive vs. experiential negative symptoms. Moderator analyses evaluated a broad array of treatment and participant factors. The meta-analysis (5319 participants) revealed that CR produced a small effect size improvement on summary negative symptoms (Hedge's g = 0.179). Sample differences in age, duration of illness, symptoms, and antipsychotic dosage did not serve as a barrier to treatment benefit. CR also produced small-to-moderate improvements in alogia (Hedge's g = 0.312) but not experiential negative symptoms. Programs of CR that utilized bridging activities that relate training of cognitive skills to activities of daily living produced greater improvement in negative symptoms (g = 0.281 vs 0.055). Longer CR programs also produce larger effects on negative symptoms. CR produces small, consistent reductions in negative symptoms in people with schizophrenia. Variations in CR effects may be linked to different treatment ingredients.

Расчетно-экспериментальное исследование и идентификация матричной модели струйного измельчения сыпучих материалов

Grinding powders in jet mills makes it possible to obtain materials uncontaminated by wear products of grinding media to prepare pure products in chemical and pharmaceutical industries. It should be noted that for different technologies, the requirements for the granulometric composition of powders vary significantly, and to meet these requirements, specific approaches are often required. Experimental determination of optimal conditions to obtain new powders is often associated with significant costs, which is not always justified from an economic point of view. Mathematical modeling allows us to obtain effective solutions in a shorter time and at lower costs in most cases. Thus, the development of adequate models, methods of calculation and optimization of jet grinding technology is an urgent scientific and practical task. To simulate grinding, a matrix methodology is used to describe technological processes. To identify the model, the results of specially conducted experimental studies are used. During experimental studies, the granulometric compositions of powders after single and multiple grinding in a jet mill have been determined. The results obtained have been used to identify and verify the matrix grinding model. Within the framework of the resulting model, a method for technological calculation of a jet mill operating in an open grinding cycle has been developed. The presented approach allows us to propose the ways to develop and implement new jet grinding technologies to obtain highly pure products in open and closed grinding cycles.

Enhancing Mandarin Tone and Vowel Recognition in Cochlear Implant Simulation through Combining Task Practice

Purpose: Mandarin tone recognition with cochlear implant simulation can be enhanced via targeted auditory training. However, such training generally does not transfer to different tasks, even when they share the same stimuli. As such, this study investigated whether combined Mandarin tone and vowel recognition training leads to greater improvements in Mandarin tone, vowel, and sentence recognition compared to tone- or vowel- only training.Methods: Twenty-nine native Mandarin-speaking young adults with normal hearing were randomly assigned to one of three training regimens: Tone-Only, Vowel-Only, Tone-Vowel. Mandarin tone, vowel, and sentence recognition were tested before and after training.Results: Tone-Only training improved Mandarin tone recognition but not vowel recognition, while Vowel-Only training improved Mandarin vowel recognition but not tone recognition. The combined Tone-Vowel training improved both tone and vowel recognition. ToneVowel training also led to improvements in Mandarin sentence recognition, although these gains were not significantly greater than those observed in Tone-Only or Vowel-Only groups.Conclusion: These findings suggest that within an equivalent amount of training, combined tone and vowel recognition training enhances both tone and vowel recognition, while tone- or vowel-only training improves the performance in the targeted task alone. Combined tone and vowel recognition may be necessary to achieve improvements in Mandarin tone, vowel, and sentence recognition.

Perceived Supervisor Support and Academic Procrastination in Postgraduate Students: Roles of Basic Psychological Needs Satisfaction and Learning Engagement

Academic procrastination is a common problem among postgraduate students, one that has caused negative consequences that cannot be ignored. Therefore, finding out how to effectively eliminate academic procrastination behavior has become an essential task in educational practice. Based on the use of Self-Determination Theory (SDT) to explore the relationship between the perceived supervisor support and academic procrastination of postgraduate students, a total of 448 questionnaires were gathered from postgraduate students across China. The results showed significant correlations between the perceived supervisor support, basic psychological needs satisfaction, learning engagement, and academic procrastination of postgraduate students. The relationship between the perceived supervisor support and academic procrastination of postgraduate students was mediated by basic psychological needs satisfaction and learning engagement. Additionally, basic psychological needs satisfaction and learning engagement played a chain-mediating role between the perceived supervisor support and academic procrastination of postgraduate students. The findings of this study contributed to the enhancement of the interaction between supervisors and students in higher education and improving the quality of postgraduate education.

Transparency in action or political fiction? The pre-inquiry phase of the UK COVID-19 Inquiry

AbstractThis article examines how public inquiries come into being through a detailed analysis of the pre-inquiry phase of the UK COVID-19 Inquiry. We focus on two key points of contestation—around the timing of the inquiry and its terms of reference—and suggest that what is at stake during the pre-stage of an inquiry is its ontological and epistemological orientation. Our analysis traces the emergence of two distinct positions during this phase: a grassroots position (conceiving of the inquiry as a response to the experience of collective grief) and an official position (conceiving of the inquiry as a means of learning lessons). Particular attention is given to the latter position and what it reveals about the rhetorical function of transparency in confirming a political commitment to institutional accountability, at the same time as working towards a particular conception of the inquiry as a practical task, expansive in remit, and future oriented.

If you fit, you commit? The relationship between the organizational climate and employees’ commitment: the moderating role of the person-environment fit

PurposeBased on the theory of organizational socialization, this article broadens the knowledge in the field of organizational commitment by determining the relationship between the organizational climate (OC) and the employees’ commitment, as well as the moderating role of the person-environment (P-E) fit.Design/methodology/approachWe conducted quantitative research using three psychometric questionnaires. We investigated a large sample (N = 1,032) of employees hired in Poland.FindingsWe found strong relationships between the OC, the employees’ fit and their commitment. Moreover, both supplementary and complementary fit significantly moderated the relationships between the majority of climate dimensions and, especially, affective commitment. Interestingly, highly fitted employees with longer tenure showed a stronger relationship between material climate dimensions and commitment compared to similarly fitted newcomers, for whom the most important were relationships with co-workers and superiors.Research limitations/implicationsWe analyzed only a subjective fit among employees working in Poland. Although it was beneficial for developing the OC knowledge of non-American sample, the results require cautious generalization.Practical implicationsAssessing a candidate’s fit with the organization through detailed interviews, behavioral questions or practical tasks during the selection process can improve candidates' and employees’ P-E fit. A better fit can increase commitment, even if the OC or other factors are not perfect. Socialization tactics aimed at strengthening the fit can facilitate better alignment with the climate and higher commitment among employees with longer tenure.Originality/valueThis study is the first to empirically verify the moderating role of the P-E fit on the relations between OC and organizational commitment. It also considers the comparison between more experienced employees and newcomers.

Addressing deficits in malnutrition care in northern Sierra Leone; an educational intervention

Abstract The 2017 Sierra Leone National Nutrition Survey reported poor practice regarding the medical management of severe acute malnutrition and the provision of emergency food to patients, particularly in the northern city of Makeni. We developed a training package for paediatric nurses, aiming to design an easily reproducible format which could be utilised nationally. Our aims were to empower and educate staff to provide more consistent care by challenging the negative cultural perception of malnourished patients and increasing knowledge of key guidelines. A two-day course based around UNICEF guidance on malnutrition was developed with the financial support of the Sierra Leonean office of the World Health Organisation. We sought the advice of local staff on issues impacting the quality of malnutrition care, including cultural barriers, educational limitations and supply issues. Lectures and practical sessions were devised covering recognition, assessment and management. Communication skills were taught using role play, focussing on the key role of nurses in parental education, and nursing skills were reinforced using practical tasks. Local staff identified for their strong teaching and leadership were recruited as instructors and to provide feedback on session design and content. 21 candidates attended from across the region, with the majority based in inpatient feeding units. Candidates completed a pre- and post-course written examination and a qualitative feedback form. Pre-course exam results ranged from 35%-85% with an average of 65%; post-course results showed improvement with a range of 55%-100% and an average of 88%. Candidates rated the course on average 9.7 out of 10 and the written feedback was positive. We subsequently observed positive changes in clinical practice in the inpatient feeding units across the region. Based on these assessments the course achieved its aims. Further study is needed to know if noted clinical improvements were sustained. Key messages • Challenging local cultural perceptions of malnourished patients is crucial to establishing improvement in medical care for this group. • Sustainable educational intervention requires local instructors who can independently deliver teaching, and establishing this cohort at the course inception is critical to its ongoing success.

Harnessing Virtual Reality: Improving Social Skills in Adults with Autism Spectrum Disorder

Background and Objectives: Autism Spectrum Disorder (ASD) involves challenges in social communication and daily functioning. Emerging research highlights that virtual reality (VR) interventions can significantly improve social skills in adults with ASD by providing immersive, controlled practice environments. This systematic review will assess the effectiveness of VR-based interventions for improving social skills in adults with ASD. Materials and Methods: Studies were identified from an online search of PubMed, Web of Science, Cochrane Library, and Embase databases without any search time range. This review was registered on Open OSF (n) P4SM5. Results: Recent studies show that VR interventions significantly enhance job interview skills, social abilities, and practical tasks in adults with ASD, with improvements in confidence, social understanding, and everyday skills. VR has been shown to be user-friendly and effective in providing immersive, adaptable training experiences. Conclusions: The review highlights VR’s promising role in improving social skills, job interview abilities, and daily functioning in adults with ASD. It emphasizes the need for broader studies, standardized interventions, and exploration of VR’s integration with other therapies to enhance long-term effectiveness and address comorbidities like anxiety and depression.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

Robot learning on the job: Human-in-the-loop autonomy and learning during deployment

With the rapid growth of computing powers and recent advances in deep learning, we have witnessed impressive demonstrations of novel robot capabilities in research settings. Nonetheless, these learning systems exhibit brittle generalization and require excessive training data for practical tasks. To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work. In this framework, partially autonomous robots are tasked with handling a major portion of decision-making where they work reliably; meanwhile, human operators monitor the process and intervene in challenging situations. Such a human–robot team ensures safe deployments in complex tasks. Further, we introduce a new learning algorithm to improve the policy’s performance on the data collected from the task executions. The core idea is re-weighing training samples with approximated human trust and optimizing the policies with weighted behavioral cloning. We evaluate Sirius in simulation and on real hardware, showing that Sirius consistently outperforms baselines over a collection of contact-rich manipulation tasks, achieving an 8% boost in simulation and 27% on real hardware than the state-of-the-art methods in policy success rate, with twice faster convergence and 85% memory size reduction. Videos and more details are available at https://ut-austin-rpl.github.io/sirius/ .

Adaptive teaching: Latin and the use of digital parsing tools

Abstract The Initial Teacher Training (ITT) Common Core Framework (CCF) requires all teachers to adapt classroom teaching to support all students (Department for Education, 2024). While the Department for Education's previously-encouraged concept of ‘differentiation’ was well-intentioned, in that tasks were meant to be graded to support and develop all students' learning in a class where there were many different levels of prior attainment, it tended to mean that in practice in the classroom different tasks were often set to cater to the supposed different learning attributes of students. Differentiation has now been superseded by the concept of ‘adaptive teaching’. Adaptive teaching is characterised by teaching that takes account of the range in prior attainment of students and anticipates barriers to learning by providing resources or specific support so that they may all achieve the same intended outcomes. Classics teaching is often characterised by the reading of large amounts of text, both in the original languages of Latin or ancient Greek or in translation. Students in the Classics classroom contain a wide range of prior attainment, cultural experiences and may have barriers to learning, such as Special Educational Needs and Disabilities. This article presents findings of how the use of a blended language approach, involving the digital parsing tools in the Cambridge Latin Course and Suburani Latin course books, improved the quantity of translation of Latin achieved by students of mixed prior attainment and encouraged a more effective use of classroom time to develop deeper comprehension and understanding of the translated texts.

Sleep Fragmentation Modulates the Neurophysiological Correlates of Cognitive Fatigue.

Cognitive fatigue (CF) is a critical factor affecting performance and well-being. It can be altered in suboptimal sleep quality conditions, e.g., in patients suffering from obstructive sleep apnea who experience both intermittent hypoxia and sleep fragmentation (SF). Understanding the neurophysiological basis of SF in healthy individuals can provide insights to improve cognitive functioning in disrupted sleep conditions. In this electroencephalographical (EEG) study, we investigated in 16 healthy young participants the impact of experimentally induced SF on the neurophysiological correlates of CF measured before, during, and after practice on the TloadDback, a working memory task tailored to each individual's maximal cognitive resources. The participants spent three consecutive nights in the laboratory two times, once in an undisrupted sleep (UdS) condition and once in an SF condition induced by non-awakening auditory stimulations, counterbalanced and performed the TloadDback task both in a high (HCL) and a low (LCL) cognitive load condition. EEG activity was recorded during wakefulness in the 5 min resting state immediately before and after, as well as during the 16 min of the TloadDback task practice. In the high cognitive load under a sleep-fragmentation (HCL/SF) condition, high beta power increased during the TloadDback, indicating heightened cognitive effort, and the beta and alpha power increased in the post- vs. pre-task resting state, suggesting a relaxation rebound. In the low cognitive load/undisturbed sleep (LCL/UdS) condition, low beta activity increased, suggesting a relaxed focus, as well as mid beta activity associated with active thinking. These findings highlight the dynamic impact of SF on the neurophysiological correlates of CF and underscore the importance of sleep quality and continuity to maintain optimal cognitive functioning.

Doubly Optimal Parallel Wire Cutting without Ancilla Qubits

A restriction in the quality and quantity of available qubits presents a substantial obstacle to the application of near-term and early fault-tolerant quantum computers in practical tasks. To confront this challenge, some techniques for effectively augmenting the system size through classical processing have been proposed; one promising approach is quantum circuit cutting. The main idea of quantum circuit cutting is to decompose an original circuit into smaller subcircuits and combine outputs from these subcircuits to recover the original output. Although this approach enables us to simulate larger quantum circuits beyond physically available circuits, it needs classical overheads quantified by two metrics: the sampling overhead in the number of measurements to reconstruct the original output, and the number of channels in the decomposition. Thus, it is crucial to devise a decomposition method that minimizes both of these metrics, thereby reducing the overall execution time. This paper studies the problem of decomposing the n-qubit identity channel, i.e., n-parallel wire cutting, into a set of local operations and classical communication; then we give an optimal wire-cutting method composed of channels based on mutually unbiased bases that achieves minimal overheads in both the sampling overhead and the number of channels, without ancilla qubits. This is in stark contrast to the existing method that achieves the optimal sampling overhead yet with ancilla qubits. Moreover, we derive a tight lower bound on the number of channels in parallel wire cutting without ancilla systems and show that only our method achieves this lower bound among the existing methods. Notably, our method shows an exponential improvement in the number of channels, compared to the aforementioned ancilla-assisted method that achieves optimal sampling overhead. Our work significantly alleviates the additional overheads for the wire-cutting method and may provide essential components for the early success of quantum computing. Published by the American Physical Society 2024

Practical Probabilistic Model-Based Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling.

This article addresses the prediction stability, prediction accuracy, and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach to dropout-based probabilistic ensembles with trajectory sampling (DPETS) is proposed, where the system uncertainty is stably predicted by combining the Monte Carlo dropout (MC Dropout) and trajectory sampling in one framework. Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models. The state propagation in its policy is extended to filter the aleatoric uncertainty for superior control capability. Evaluated by several Mujoco benchmark control tasks under additional disturbances and one practical robot arm manipulation task, DPETS outperforms related MBRL approaches in both average return and convergence velocity while achieving superior performance than well-known model-free baselines with significant sample efficiency. The open-source code of DPETS is available at https://github.com/mrjun123/DPETS.

Национальные традиции в фольклоре разных народов России: особенности изучения в начальной школе

The article discusses the problem of studying folklore of different peoples of Russia by primary school students. The article presents variants of tasks that orient students to analytical work with small genres of folklore, study of the features of the language of proverbs and sayings as a means of displaying the national way of thinking, features, life, customs, beliefs. Practical tasks allow to expand the horizons of the primary schoolchildren: to visualize the geographical location of the people, the connection of their way of life with nature, way of thinking, traditions, life values. Each task is supplemented with a methodological commentary for the teacher.

Weakly supervised deep learning for diagnosis of prostate cancer after radical prostatectomy: a population- based, multicenter study

Abstract Introduction/Objective The pathological diagnosis of prostate cancer after radical prostatectomy (RP) is a challenging and time-consuming process. Artificial intelligence has the potential to enhance this situation significantly. Our study aimed to assess the efficacy of weakly supervised deep learning, specifically focusing on practical diagnostic tasks for RP specimens. Methods/Case Report We obtained slices from clinical centers across four regions in China. The model was developed based on a multiple instance learning framework. We evaluated the model’s predictive accuracy, covering both slice-level and lesion-level diagnoses. Additionally, we investigated lesion localization using attention visualization. Slice-level diagnosis included prostate cancer detection and Gleason grading, while lesion-level diagnosis involved recognizing index lesions and non-index lesions. Results (if a Case Study enter NA) The study involved 13,245 slides from 304 patients. The area under the curve values in four validation sets ranged from 0.921 (95% CI 0.913–0.930) to 0.964 (95% CI 0.959–0.968). Regarding Gleason grading, the model demonstrated quadratic Weighted Kappa values of 0.748 and 0.720 for internal and external validation, respectively. The model predicted 1,683 (35.3%) positive and 3,081 (64.7%) negative slices in all validation sets. The median area of index lesions and non-index lesions was 92.6mm2 (IQR: 54.2–130.0mm2) and 3.5mm2 (IQR: 1.4–10.9mm2) (p<0.001), respectively. The model accurately predicted index lesions in all cases for three validation sets. Furthermore, the model achieved 100% accuracy in predicting slides with the maximum tumor area for index lesions in two validation sets. In visual assessment, 200 randomly selected slices predicted as true positive demonstrated that the model could identify 97.5% (195/200) of the index lesions. Conclusion The weakly supervised deep learning model demonstrated remarkably high diagnostic and localization capabilities in identifying index lesions within RP specimens. This targeted approach would effectively alleviate the diagnostic workload associated with pathology assessments for prostate cancer.