AI-based Assessment Research Articles

Assessment and treatment of visuospatial neglect using active learning with Gaussian processes regression

Visuospatial neglect is a disorder characterised by impaired awareness for visual stimuli located in regions of space and frames of reference. It is often associated with stroke. Patients can struggle with all aspects of daily living and community participation. Assessment methods are limited and show several shortcomings, considering they are mainly performed on paper and do not implement the complexity of daily life. Similarly, treatment options are sparse and often show only small improvements.We present an artificial intelligence solution designed to accurately assess a patient’s visuospatial neglect in a three-dimensional setting. We implement an active learning method based on Gaussian process regression to reduce the effort it takes a patient to undergo an assessment. Furthermore, we describe how this model can be utilised in patient oriented treatment and how this opens the way to gamification, tele-rehabilitation and personalised healthcare, providing a promising avenue for improving patient engagement and rehabilitation outcomes.To validate our assessment module, we conducted clinical trials involving patients in a real-world setting. We compared the results obtained using our AI-based assessment with the widely used conventional visuospatial neglect tests currently employed in clinical practice. The validation process serves to establish the accuracy and reliability of our model, confirming its potential as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR application proves to be more sensitive, while intra-rater reliability remains high.

Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation.

Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction's (DEJ's) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts' visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients.

Exploring the potential of artificial intelligence tools in educational measurement and assessment

Artificial intelligence (AI) is transforming various industries, and education is no exception. Rapid advancements in AI technology have become essential for educators and educational assessment professionals to enhance teaching and learning experiences. AI-powered educational assessment tools provide numerous benefits, including improving the accuracy and efficiency of assessments, generating personalized feedback for students, and enabling teachers to adapt their teaching strategies to meet the unique needs of each student. Therefore, AI has the potential to revolutionize the way education is delivered and assessed, ultimately leading to better educational outcomes for students. This paper explores the various applications of AI tools in educational measurement and assessment. Specifically, it discusses the integration of large language AI models in classroom assessment, in specific areas such as test purpose determination and specification, developing, test blueprint, test item generation/development, preparation of test instructions, item assembly/selection, test administration, test scoring, interpretation of test results, test analysis/appraisal, and reporting. It analyses the role of teachers in AI-based assessment and the challenges of using AI-powered tools in educational assessment. Finally, the paper presents strategies to address these challenges and enhance the effectiveness of AI in educational assessment. In conclusion, using AI in educational assessment has benefits and limitations. As such, educators, policymakers, and stakeholders must work together to develop strategies that maximize the benefits of AI in educational assessment while mitigating the associated risks. The application of AI in educational assessment can ultimately transform education, improve learning outcomes, and equip students with the skills needed to succeed in the 21st century.

Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting

AimDiabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.MethodsIt was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.ResultsThe agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979).ConclusionsThe Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns.

P–228 AI-based assessment of embryo viability correlates with features of embryo ploidy

Abstract Study question Do AI models used to assess embryo viability (based on pregnancy outcome) also correlate with known embryo quality measures such as ploidy status? Summary answer An AI for embryo viability assessment correlated with ploidy status, and with karyotypic features of aneuploidy, supporting its use for embryo selection. What is known already One factor that can influence pregnancy success is the genetic status of the embryo. PGT-A is commonly used to test for embryo ploidy, with the aim of identifying karyotypically normal embryos (euploid embryos), for preferential transfer. There is evidence suggesting that transfer of euploid embryos produces favorable clinical outcomes over aneuploid embryos. Given the AI model was trained to evaluate clinical pregnancy, it was hypothesized that the score might also correlate with ploidy status, and with different types of aneuploidies. Little is known about morphological correlations with embryo ploidy status, so we also sought to explore this relationship. Study design, size, duration This study involved analysis of a retrospective dataset of single static Day 5 embryo (blastocyst) images with associated PGT-A results and AI viability scores. The dataset comprised images of 5,469 embryos from 2,615 consecutive patients treated at five US IVF clinics between February 2015 and April 2020. The AI was trained on thousands of Day 5 embryo images from multiple IVF laboratories in multiple countries, but was not trained on data used in this study. Participants/materials, setting, methods Average patient age was 36.2 years, and average embryo cohort size was 2.1/patient. PGT-A analysis was performed on embryos at time of evaluation. The dataset comprised 3,251 (59.4%) euploid embryos, 1,815 (33.2%) aneuploid embryos, and 403 (7.4%) mosaic embryos. The AI was retrospectively used to provide a score between 0 (predicted non-viable) and 10 (predicted viable) for each image. Correlation between the AI viability score and euploid, mosaic and aneuploid embryos was then assessed. Main results and the role of chance Results showed a statistically significant correlation between AI viability score and PGT-A outcome, consistent with a relationship between pregnancy outcome and ploidy status. The average score for euploid embryos was 8.20, which was significantly higher than the average score for aneuploid embryos of 7.80 (p < 0.0001). There was a significant linear increase in confidence score from full aneuploid embryos, through mosaic embryos (average score 7.97), to full euploid embryos (mosaic threshold of 20–80%). High mosaic embryos tended to have a lower average score (7.60) than low mosaic embryos (7.96), consistent with correlation of viability (pregnancy outcome) with the degree of mosaicism. AI viability score also correlated with ploidy features believed to affect pregnancy outcomes. Trisomic changes had higher average scores than monosomic changes. Segmental changes had higher average scores than full gain or loss. The AI score differentiated euploid from aneuploid status more efficiently in embryos with poorer morphology than those with good morphology. Whilst there was an evident correlation between pregnancy outcome and ploidy status, the AI was only weakly predictive of euploidy, with an accuracy of 57.3% using an AI viability score threshold of 7.5/10.This suggests pregnancy-related morphological features are somewhat correlated with embryo ploidy, but not completely. Limitations, reasons for caution The PGT-A technique is held to have some limitations for evaluating ploidy status, therefore it would be of benefit to perform additional confirmatory studies on independent datasets. It would be of interest to conduct prospective studies evaluating correlations between the AI’s evaluation of morphology and pregnancy outcome with ploidy status. Wider implications of the findings: The AI score correlated with genetic features of embryos that are known to correlate with pregnancy, which further supports the efficacy and use of AI for embryo viability assessment. The AI identified morphological features that are somewhat predictive of ploidy status, with potential application to embryos of poorer Gardner score. Trial registration number none

AI-Based Assessment for Teaching and Learning Enhancement

This paper explores the use of Artificial Intelligence in the smart education learning process. Nowadays Artificial Intelligence grapes a place in the education field. This survey is giving justification that how the humans' mind is changed by AI. Here we go on the proposed model, experimentation, results, and Limitation of the system. These papers present various methods of Artificial Intelligence in student assessment for teacher and learning enhancement.