Class Activities Research Articles

THE DEVELOPMENT OF VIRTUAL VIDEO ANIMATED BASED ON ENGLISH TEACHING MEDIA FOR THE EIGHTH STUDENTS AT JUNIOR HIGH SCHOOL FITRA ABDI PALEMBANG

This research aims to develop virtual animated video-based teaching media to increase students' interest in learning and understanding in English teaching and learning activities in class VIII SMP Fitra Abdi Palembang. The method used in this research is research and development with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Trials using a questionnaire showed that this media was very interesting with a percentage of 91.9% according to the attractiveness criteria. The results of the pre-test and post-test conducted on 30 students showed significant improvement. In the pre-test, only 7 students achieved scores according to the criteria for completeness of learning outcomes. After learning using virtual animated videos, 28 students achieved scores above the criteria for learning completeness (70), while 2 students were still below these criteria. The overall class avera1ge increased to 79.3. It can be concluded that virtual English video animation media is effective and successful in improving student learning outcomes and their interest in the learning process.

Combined graph convolutional networks with a multi-connection pattern to identify tremor-dominant Parkinson's disease and essential tremor with resting tremor.

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

Abstract Su803: Sudden Cardiac Arrest Prediction via Deep Learning Electrocardiogram Analysis

Background: Sudden cardiac arrest (SCA) is a potentially fatal event that often occurs without prior indications. Hypothesis: It was hypothesized that electrocardiogram (ECG) data could be used in conjunction with Deep Learning (DL) algorithms to predict the occurrence of SCA’s. Aims: To improve SCA outcomes and enable preventative strategies, the ECG data from the Nightingale Open Science - Subtyping Cardiac Arrest dataset were utilized to create a DL model to serve as a screening tool for sudden cardiac arrest. Methods: A publicly-available dataset containing ten seconds of 12-lead ECGs from individuals who had a SCA and persons who did not have a SCA, and information about time from ECG to arrest, and age and sex were utilized for individualized prediction of SCA. Subsets of the initial data were used to train, validate, and test deep convolution neural network models that processed cardiac cycles from single-lead ECG voltage waveforms. Results: The cohort comprised 221 (mean age: 68.5 and #females: 75) patients who experienced SCA and 1046 controls (mean age: 69.7 and #females: 497) who did not have a cardiac arrest. The SCA ECG-DL base model, with ECGs within one day and with age and sex data, had an area under the receiver operating curve (AUROC) of 0.774 during hold-out testing for discrimination of subsequent SCA. With sensitivity set at 95%, the base model specificity of the SCA ECG-DL model was 31%. Gradient-weighted class activation mapping showed that the SCA ECG-DL model relied most heavily on the QRS complex to make predictions. The performance of the SCA ECG-DL model was similar removing age and sex data (AUROC 0.775) and less reliable when predicting SCA within one month (AUROC 0.734) or between one month and one year (AUROC 0.574). Conclusion: DL interpretation of ECG data is a promising means of screening for SCA, and this method explains differences in SCA’s due to age and sex. Model performance improved when ECGs were obtained at shorter intervals to the incident SCA event, and SCA prediction was more dependent upon QRS complex data than other temporal segments of the ECG.

Antioxidant System of Scutellum During Germination and Early Growth of Maize Seedlings

Maize is among the world’s three most important cereals because it is used for human consumption and agricultural feed. The embryo in monocotyledons contains a cotyledon that is the scutellum, which in Chalqueño maize constitutes approximately 80% of the embryo’s mass. The activation of metabolism during germination is accompanied by the production of reactive oxygen species, which must be maintained at a low level to avoid damage. Little is known about the oxidative state of the scutellum, but it is important to understand the control of oxidative stress during the final phase of germination and the embryo–seedling transition. Among the enzymes involved are class III peroxidase (POX), catalase (CAT), and superoxide dismutase (SOD), which were observed in the scutella of isolated imbibed embryos between 0 and 36 h. The activity of SOD fluctuated over a baseline value. The activity of class III POX was greater than that of CAT, showing differences between them in germination and postgermination. The activities of CAT and POX increased during germination (0 to 18 h), stabilized towards the final phase of germination (18 to 24 h), and then increased again in postgermination (24 to 36 h). The POX activity is a biochemical marker of the scutellum metabolism and marks the transition from germination to the embryo–seedling transition.

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia. We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets. Three deep convolutional neural networks (ResNet50, DenseNet121, and EfficientNetv2-S) were used to distinguish mycoplasma pneumonia from viral pneumonia based on CXR. Accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of the model. Visualization was also achieved through the use of Class Activation Mapping (CAM), providing more transparent and interpretable classification results. Of the three models evaluated, ResNet50 outperformed the others. Pretrained with the ZhangLabData dataset, the ResNet50 model achieved 80.00% accuracy in the validation set. The model also showed robustness in two test sets, with accuracy of 82.65 and 83.27%, and AUC values of 0.822 and 0.758. In the test results using ImageNet pre-training weights, the accuracy of the ResNet50 model in the validation set was 80.00%; the accuracy in the two test sets was 81.63 and 62.91%; and the corresponding AUC values were 0.851 and 0.776. The sensitivity values were 0.884 and 0.595, and the specificity values were 0.655 and 0.814. This study demonstrates that deep convolutional networks utilizing transfer learning are effective in detecting mycoplasma pneumonia based on chest X-rays (CXR). This suggests that, in the near future, such computer-aided detection approaches can be employed for the early screening of pneumonia pathogens. This has significant clinical implications for the rapid diagnosis and appropriate medical intervention of pneumonia, potentially enhancing the prognosis for affected children.

How worked example Echo360 videos and embedded polls with students as partners improved learning outcomes in accounting

This paper reports the use of worked example videos, with students as partners, using Echo360 and containing embedded polls in a postgraduate accounting subject which offered limited opportunity for in-class practical activities. A total of twelve worked example videos were produced and deployed to assist students with their learning. Each video included embedded polls which aimed to further engage students and reinforce key concepts. Results were compared for students who engaged with the videos and polls with those who had not. Findings revealed that students who viewed the videos and participated in the polls demonstrated significant improvement in the final exam and overall marks. This study is one of the first to examine the use and benefits of using dialogue videos with embedded polls as a means of increasing learning, engagement, and overall student results.

A new guided flipped learning model for lifelong learning

ABSTRACT Flipped learning model is currently implemented where conventional lectures take place before class hours, while in-class time is used for hands-on activities and problem-solving under the teacher’s guidance. However, students’ inability to fully engage and complete their learning activities before the class hours and the lack of clear instruction during in-class learning activities can greatly hinder the learning experience. Under this circumstance, a new flipped model, named guided flipped learning (GFL) model, was proposed by embedding study-summary-quiz and think-pair-share strategies into the conventional flipped learning process to overcome the current challenges. To investigate the effectiveness of the proposed GFL model, a quasi-experimental study was conducted. The results showed that GFL significantly enhanced students’ engagement (behavioural, agentic, and emotional) and learning achievement. The proposed GFL model strengthens the quality of the pre-class learning activities and provides clear instruction for in-class learning activities. The results provide educational policy makers the means to effectively revise the conventional flipped model with GFL for lifelong learning.

Enhancing adversarial transferability with local transformation

Robust deep learning models have demonstrated significant applicability in real-world scenarios. The utilization of adversarial attacks plays a crucial role in assessing the robustness of these models. Among such attacks, transfer-based attacks, which leverage white-box models to generate adversarial examples, have garnered considerable attention. These transfer-based attacks have demonstrated remarkable efficiency, particularly under the black-box setting. Notably, existing transfer attacks often exploit input transformations to amplify their effectiveness. However, prevailing input transformation-based methods typically modify input images indiscriminately, overlooking regional disparities. To bolster the transferability of adversarial examples, we propose the Local Transformation Attack (LTA) based on forward class activation maps. Specifically, we first obtain future examples through accumulated momentum and compute forward class activation maps. Subsequently, we utilize these maps to identify crucial areas and apply pixel scaling for transformation. Finally, we update the adversarial examples by using the average gradient of the transformed image. Extensive experiments convincingly demonstrate the effectiveness of our proposed LTA. Compared to the current state-of-the-art attack approaches, LTA achieves an increase of 7.9% in black-box attack performance. Particularly, in the case of ensemble attacks, our method achieved an average attack success rate of 98.3%.

Controversial Cartoon at Central University

This fictional case study provides a summary of events that took place at Central University after the campus newspaper published a controversial political cartoon mocking media coverage of the killing of an unarmed Black man. The cartoon and lack of administrative response led to the formation of a student activist group. The case highlights the conflict some campus administrators perceive in protecting student expression, which may harm other students’ feelings of safety and belonging. Discussion questions and suggested class activities are included.

Advancing buffet onset prediction: a deep learning approach with enhanced interpretability for aerodynamic engineering

The interaction between the shock wave and boundary layer of transonic wings can trigger periodic self-excited oscillations, resulting in transonic buffet. Buffet severely restricts the flight envelope of civil aircraft and is directly related to their aerodynamic performance and safety. Developing efficient and reliable techniques for buffet onset prediction is crucial for the advancement of civil aircraft. In this study, utilizing a comprehensive database of supercritical airfoils generated through numerical simulations, a convolutional neural network (CNN) model is firstly developed to perform buffet classification based on the flow fields. After that, employing explainable machine learning techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), random forest algorithms, and statistical analysis, the research investigates the correlations between supervised CNN features and key physical characteristics related with the separation region, shock wave, leading edge suction peak, and post-shock loading. Finally, physical buffet onset metric is established with good generalization and accuracy, providing valuable guidance for engineering design in civil aircraft.

Designing a Digital Interactive Simulation for Teaching Business Analytics, Strategy, and Economics

Students can experience a challenging period of adaptability as they transition from high school to college class activities. As such, students can potentially lose interest in studying entrepreneurial economics, which often leads to difficulties in choosing the academic path that can fulfill their learning enthusiasm as they debut their college journey. The use of an educational game can potentially spark students’ interest in the study of entrepreneurial economics. This research study explores the functional areas in the design of an educational game and the theoretical framework supporting the use of a digital interactive business simulation to facilitate the practical understanding of processes involved in strategic decisions and business analytics in the study of entrepreneurial economics.

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

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. 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. 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. 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. 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.

Conflict during learning reconfigures the neural representation of positive valence and approach behavior.

Punishing and rewarding experiences can change the valence of sensory stimuli and guide animal behavior in opposite directions, resulting in avoidance or approach. Often, however, a stimulus is encountered with both positive and negative experiences. How is such conflicting information represented in the brain and resolved into a behavioral decision? We address this question by dissecting a circuit for sexual conditioning in C.elegans. In this learning paradigm, an odor is conditioned with both a punishment (starvation) and a reward (mates), resulting in odor approach. We find that negative and positive experiences are both encoded by the neuropeptide pigment dispersing factor 1 (PDF-1) being released from, and acting on, different neurons. Each experience creates a distinct memory in the circuit for odor processing. This results in the sensorimotor representation of the odor being different in naive and sexually conditioned animals, despite both displaying approach. Our results reveal that the positive valence of a stimulus is not represented in the activity of any single neuron class but flexibly represented within the circuit according to the experiences and predictions associated with the stimulus.

Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model

Our previous research papers have shown the potential of deep-learning models for real-time detection and control of porosity defects in 3D printing, specifically in the laser powder bed fusion (LPBF) process. Extending these models to identify other defects like surface deformation poses a challenge due to the scarcity of available data. This study introduces the use of Transfer Learning (TL) to train models on limited data for high accuracy in detecting surface deformations, marking the first attempt to apply a model trained on one defect type to another. Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. This results in faster training and evaluation, ideal for real-time systems with limited computing capabilities. We further employed Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the model’s decision-making, highlighting the areas influencing defect detection. This step is vital for developing a trustworthy model, showcasing the effectiveness of our approach in broadening the model’s applicability while ensuring reliability and efficiency.

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays

Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial sutures in infants, is crucial for effective treatment. Although computed topography offers detailed imaging, its high radiation poses risks, especially to children. Therefore, we propose a deep-learning model for CSO and suture-line classification using 2D cranial X-rays that minimises radiation-exposure risks and offers reliable diagnoses. We used data comprising 1,047 normal and 277 CSO cases from 2006 to 2023. Our approach integrates X-ray-marker removal, head-pose standardisation, skull-cropping, and fine-tuning modules for CSO and suture-line classification using convolution neural networks (CNNs). It enhances the diagnostic accuracy and efficiency of identifying CSO from X-ray images, offering a promising alternative to traditional methods. Four CNN backbones exhibited robust performance, with F1-scores exceeding 0.96 and sensitivity and specificity exceeding 0.9, proving the potential for clinical applications. Additionally, preprocessing strategies further enhanced the accuracy, demonstrating the highest F1-scores, precision, and specificity. A qualitative analysis using gradient-weighted class activation mapping illustrated the focal points of the models. Furthermore, the suture-line classification model distinguishes five suture lines with an accuracy of > 0.9. Thus, the proposed approach can significantly reduce the time and labour required for CSO diagnosis, streamlining its management in clinical settings.

A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment. This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources. A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants. The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777). We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.

AUTOMATED INDOOR ACTIVITY MONITORING FOR ELDERLY AND VISUALLY IMPAIRED PEOPLE USING QUANTUM SALP SWARM ALGORITHM WITH MACHINE LEARNING

Elderly and vision-impaired indoor activity monitoring uses sensor technology to monitor interaction and movement in the living area. This system can identify deviations from regular patterns, provide alerts, and ensure safety in case of emergencies or potential risks. These solutions enhance quality of life by promoting independent living while offering peace of mind to loved ones and caregivers. Visual impairments have become the chief part of these problems over the past few years. Usually, visually impaired people seek help from others to keep doing their daily tasks. An automated human activity recognition (HAR) technique could enhance a vision-impaired person’s transaction activity and safe movement. HAR using deep learning (DL) includes training neural networks to automatically detect and classify various activities carried out by humans based on sensor information. By leveraging DL approaches, the HAR system could accurately identify and classify activities, including sitting, standing, walking, or running, from the information captured by sensors or wearable devices. This system is integral to smart home automation, healthcare, and sports analytics. This study develops an Automated Fractal Quantum Salp Swarm Algorithm with Machine Learning based HAR (QSSA-MLHAR) for assisting elderly and visually impaired people. The QSSA-MLHAR technique uses sensory input data to identify human activities in diverse classes. In the QSSA-MLHAR technique, the min–max scalar is primarily used to scale the input data. Besides, the QSSA-MLHAR technique applies a mixed extreme learning machine (M-ELM) model to classify various human actions. The QSSA-based hyperparameter tuning process is used to improve the detection results of the M-ELM system. A wide range of simulations was performed to ensure the better performance of the QSSA-MLHAR method. The comprehensive outcomes assured that the QSSA-MLHAR technique performs well compared to other models.

Evaluation of burning detection using modified VGG19 for LULC classification changes

Abstract. This study aims to evaluate the effectiveness of a modified architecture of convolutional neural network (CNN) VGG19 for detecting fires and changes in land use and land cover classification (LULC). Remote sensing data from the Landsat 8 Operational Land Imagery (OLI) satellite was used to collect images from two distinct regions, one of which was used to obtain a dataset containing 1000 labeled images, and the other region was used to perform inference and verify the generalization of the model in an area with a high annual occurrence of fires. Analyses were conducted using a time series of normalized difference vegetation index (NDVI) and complementary cumulative distribution function (CCDF), to determine the potential for analysis in that area and define the periods of burning, pre-burning and post-burning. The VGG19 architecture was modified to maintain the input sizes of the images, resulting in a significant increase of 20.90 percentage points in the F1 score compared to the original architecture, as well as a 68.76% reduction in convergence time. In addition, the Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to improve the interpretability of the model at the moment of inference. The proposed methodology offers an approach for detecting burns by altering the LULC classification, and the modified VGG19 showed superior results.

A combination of peripherally restricted CB1 and CB2 cannabinoid receptor agonists reduces bladder afferent sensitisation in cystitis.

Cannabinoid agonists can potentially ameliorate lower urinary tract symptoms (LUTS), including pain associated with interstitial cystitis/bladder pain syndrome (IC/BPS). This study aims to determine the contributions of the cannabinoid 1 receptors (CB1Rs) and CB2Rs in regulating the activity of different functional classes of afferents, comparing normal healthy bladder with bladders from guinea pigs with protamine/zymosan-induced cystitis. The mechanosensitivity of different functional afferent classes was determined by ex vivo single-unit extracellular recordings.Peripherally restricted CB1R preferential agonists, ACEA and PrNMI and peripherally restricted CB2R selective agonists, 4Q3C and olorinab all reduced the mechanosensitivity of mucosal bladder afferents. The potency and efficacy of these synthetic cannabinoid agonists were significantly increased in cystitis compared to controls. Combined application of CB1R agonists, ACEA or PrNMI with the CB2R agonist, 4Q3C produced additive inhibitory effects. ACEA and PrNMI also inhibited the stretch-induced firing of high-threshold muscular bladder afferents in animals with cystitis. In contrast, low- and high-threshold muscular-mucosal bladder afferents were unaffected by the CB1R and CB2R agonists in control and cystitis. Our data indicated that peripherally restricted CB1R and CB2R agonists effectively reduce the sensitisation of probable nociceptive afferents in the bladder in cystitis. The findings also suggest a potential benefit of simultaneously targeting both the CB1Rs and CB2Rs to ameliorate LUTS in cystitis.

Differences in regions of interest to identify deeply invasive colorectal cancers: Computer-aided diagnosis vs expert endoscopists.

Background and study aims Diagnostic performance of a computer-aided diagnosis (CAD) system for deep submucosally invasive (T1b) colorectal cancer was excellent, but the "regions of interest" (ROI) within images are not obvious. Class activation mapping (CAM) enables identification of the ROI that CAD utilizes for diagnosis. The purpose of this study was a quantitative investigation of the difference between CAD and endoscopists. Patients and methods Endoscopic images collected for validation of a previous study were used, including histologically proven T1b colorectal cancers (n = 82; morphology: flat 36, polypoid 46; median maximum diameter 20 mm, interquartile range 15-25 mm; histological subtype: papillary 5, well 51, moderate 24, poor 2; location: proximal colon 26, distal colon 27, rectum 29). Application of CAM was limited to one white light endoscopic image (per lesion) to demonstrate findings of T1b cancers. The CAM images were generated from the weights of the previously fine-tuned ResNet50. Two expert endoscopists depicted the ROI in identical images. Concordance of the ROI was rated by intersection over union (IoU) analysis. Results Pixel counts of ROIs were significantly lower using 165K[x103] [108K-227K] than by endoscopists (300K [208K-440K]; P < 0.0001) and median [interquartile] of the IoU was 0.198 [0.024-0.349]. IoU was significantly higher in correctly identified lesions (n = 54, 0.213 [0.116-0.364]) than incorrect ones (n=28, 0.070 [0.000-0.2750, P = 0.033). Concusions IoU was larger in correctly diagnosed T1b colorectal cancers. Optimal annotation of the ROI may be the key to improving diagnostic sensitivity of CAD for T1b colorectal cancers.