Average Reading Time Research Articles

Clinical Utility of Deep Learning Assistance for Detecting Various Abnormal Findings in Color Retinal Fundus Images: A Reader Study.

To evaluate the clinical usefulness of a deep learning-based detection device for multiple abnormal findings on retinal fundus photographs for readers with varying expertise. Fourteen ophthalmologists (six residents, eight specialists) assessed 399 fundus images with respect to 12 major ophthalmologic findings, with or without the assistance of a deep learning algorithm, in two separate reading sessions. Sensitivity, specificity, and reading time per image were compared. With algorithmic assistance, readers significantly improved in sensitivity for all 12 findings (P < 0.05) but tended to be less specific (P < 0.05) for hemorrhage, drusen, membrane, and vascular abnormality, more profoundly so in residents. Sensitivity without algorithmic assistance was significantly lower in residents (23.1%∼75.8%) compared to specialists (55.1%∼97.1%) in nine findings, but it improved to similar levels with algorithmic assistance (67.8%∼99.4% in residents, 83.2%∼99.5% in specialists) with only hemorrhage remaining statistically significantly lower. Variances in sensitivity were significantly reduced for all findings. Reading time per image decreased in images with fewer than three findings per image, more profoundly in residents. When simulated based on images acquired from a health screening center, average reading time was estimated to be reduced by 25.9% (from 16.4 seconds to 12.1 seconds per image) for residents, and by 2.0% (from 9.6 seconds to 9.4 seconds) for specialists. Deep learning-based computer-assisted detection devices increase sensitivity, reduce inter-reader variance in sensitivity, and reduce reading time in less complicated images. This study evaluated the influence that algorithmic assistance in detecting abnormal findings on retinal fundus photographs has on clinicians, possibly predicting its influence on clinical application.

Can AI generate diagnostic reports for radiologist approval on CXR images? Amulti-reader and multi-case observer performance study.

Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming. To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study. Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports. Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P < 0.001), while the difference in specificity is small (0.22%) and without statistical significance (P = 0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P < 0.001). This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.

A predictive model for stunting among children under the age of three.

In light of the global effort to eradicate stunting in childhood, the objective of this research endeavor was to assess the prevalence of stunting and associated factors, simultaneously construct and validate a risk prediction model for stunting among children under the age of three in Shenzhen, China. Using the stratified random sampling method, we selected 9,581 children under the age of three for research and analysis. The dataset underwent a random allocation into training and validation sets, adhering to a 8:2 split ratio. Within the training set, a combined approach of LASSO regression analysis and binary logistic regression analysis was implemented to identify and select the predictive variables for the model. Subsequently, model construction was conducted in the training set, encompassing model evaluation, visualization, and internal validation procedures. Finally, to assess the model's generalizability, external validation was performed using the validation set. A total of 684 (7.14%) had phenotypes of stunt. Utilizing a combined approach of LASSO regression and logistic regression, key predictors of stunting among children under three years of age were identified, including sex, age in months, mother's education, father's age, birth order, feeding patterns, delivery mode, average daily parent-child reading time, average time spent in child-parent interactions, and average daily outdoor time. These variables were subsequently employed to develop a comprehensive prediction model for childhood stunting. A nomogram model was constructed based on these factors, demonstrating excellent consistency and accuracy. Calibration curves validated the agreement between the nomogram predictions and actual observations. Furthermore, ROC and DCA analyses indicated the strong predictive performance of the nomograms. The developed model for forecasting stunt risk, which integrates a spectrum of variables. This analytical framework presents actionable intelligence to medical professionals, laying down a foundational framework and a pivot for the conception and execution of preemptive strategies and therapeutic interventions.

A training protocol compliance of 13% was observed in a research study of clinical research professionals

ObjectiveWe attempted to conduct a randomized controlled trial of three different informed consent training formats to evaluate their effectiveness. We recruited 503 clinical research professionals, who received $50 for participation. Incidental findings showed unexpectedly low rates of compliance with completing the study training protocols, resulting in insufficient statistical power to test our original hypotheses. In this report, we conducted a secondary analysis of the data in which we characterize and evaluate the observed low compliance. This involved using literature on average reading times, speed-reading times, and video play speeds to calculate the timeframes required to complete the three training formats.ResultsOnly 13% of participants completed the training in a reasonable timeframe. Furthermore, only 46% of participants completed the training in the minimum possible timeframe. These findings lead us to ask whether online research training is effective, since no training can be effective if participants do not actually complete the training. Given extensive requirements for educational training among clinical research professionals, we feel the burden of proof is on training programs to demonstrate that they have positive effects.

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases.

The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists' performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% ( P <0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% ( P ≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

Interactive text reading: a text 2.0 implementation

In the article an interactive text-reading platform based on the Text 2.0 framework is presented. An experiment was carried out to investigate the reading behaviour of Italian language learners using this platform. Average reading time and comprehension scores did not differ across the conditions (a dedicated eye tracker PCeye Go, webcam-based eye tracking and mouse-only interaction). Additionally, participants in all the groups rated high on the perceived usefulness of the developed platform and overall reading experience. The implications of using such a tool for interactive reading are presented in this paper.

Assessment of Efficacy and Accuracy of Cervical Cytology Screening With Artificial Intelligence Assistive System

The role of artificial intelligence (AI) in pathology offers many exciting new possibilities for improving patient care. This study contributes to this development by identifying the viability of the AICyte assistive system for cervical screening, and investigating the utility of the system in assisting with workflow and diagnostic capability. In this study, a novel scanner was developed using a Ruiqian WSI-2400, trademarked AICyte assistive system, to create an AI-generated gallery of the most diagnostically relevant images, objects of interest (OOI), and provide categorical assessment, according to Bethesda category, for cervical ThinPrep Pap slides. For validation purposes, 2 pathologists reviewed OOIs from 32,451 cases of ThinPrep Paps independently, and their interpretations were correlated with the original ThinPrep interpretations (OTPI). The analysis was focused on the comparison of reporting rates, correlation between cytological results and histologic follow-up findings, and the assessment of independent AICyte screening utility. Pathologists using the AICyte system had a mean reading time of 55.14 seconds for the first 3000 cases trending down to 12.90 seconds in the last 6000 cases. Overall average reading time was 22.23 seconds per case compared with a manual reading time approximation of 180 seconds. Usage of AICyte compared with OTPI had similar sensitivity (97.89% vs 97.89%) and a statistically significant increase in specificity (16.19% vs 6.77%) for the detection of cervical intraepithelial neoplsia 2 and above lesions. When AICyte was run alone at a 50% negative cutoff value, it was able to read slides with a sensitivity of 99.30% and a specificity of 9.87%. When AICyte was run independently at this cutoff value, no sole case of high-grade squamous intraepithelial lesions/squamous cell carcinoma squamous lesion was missed. AICyte can provide a potential tool to help pathologists in both diagnostic capability and efficiency, which remained reliable compared with the baseline standard. Also unique for AICyte is the development of a negative cutoff value for which AICyte can categorize cases as “not needed for review” to triage cases and lower pathologist workload. This is the largest case number study that pathologists reviewed OOI with an AI-assistive system. The study demonstrates that AI-assistive system can be broadly applied for cervical cancer screening.

The effects of question previewing on response accuracy and text processing: An eye-movement study

The present study investigated the impact of manipulating reading strategies (i.e., reading the questions first [QF] or reading the passage first [PF]) during a reading comprehension test where we explored how reading strategy was related to student characteristics (i.e., reading achievement and working memory capacity). Participants' eye movements were monitored as they read 12 passages and answered multiple-choice questions. We examined differences in (a) response accuracy, (b) average total time on words in the text, (c) total task reading time, and (d) time reading text relevant to questions as a function of PF and QF strategies. Analyses were conducted to examine whether findings varied as a function of student characteristics (i.e., reading achievement and working memory capacity) and grade level (Grades 3, 5, and 8). Several interesting findings emerged from our study, including a limited effect of reading strategy use on response accuracy, with only eighth graders demonstrating better accuracy in the QF condition, and several demonstrations of PF leading to more efficient test-taking processes, including (a) longer average total reading times on words in the passage in the PF condition that could be associated with creating a better mental model of the text, (b) often being associated with less total-task time, and (c) being associated with more successful search strategies. Implications for providing teachers and students with strategies are discussed.

337 Bridging the Literacy Gap for Surgical Consents: An AI-Human Expert Collaborative Approach

INTRODUCTION: Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impedes patients’ comprehension. Health literacy is linked to patient outcomes, making it essential to address these issues. METHODS: Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4's potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. RESULTS: Analysis of 15 academic medical centers' consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P &lt; 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American's reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. CONCLUSIONS: This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Moreover, the framework we utilize here can be employed broadly to improve the accessibility of all patient-facing literature in medicine, emphasizing clear communication and mitigating disparities related to health literacy barriers. Ensuring AI technologies are safely incorporated into clinical practice is crucial to reach a wide range of patients, including the most vulnerable.

Korean version of the MNREAD acuity chart

To investigate the efficacy of the Korean version of the Minnesota low vision reading chart. A Korean version consisting of 38 items was prepared based on the MNREAD acuity chart developed by the University of Minnesota. A linguist composed the representative sentences, each containing nine words from second and third grade levels of elementary school. Reading ability was measured for 20–35-year-old subjects with normal visual acuity (corrected visual acuity of logMAR 0.0 or better). The maximum reading speed (words per minute [wpm]) for healthy participants, reading acuity (smallest detectable font size), and critical print size (smallest font size without reduction of reading speed) were analyzed. The average age of the subjects was 28.3 ± 2.6 years (male:female ratio, 4:16). The average reading time for 38 sentences was 3.66 ± 0.69 s, with no differences in the average maximum reading speed between sentences (p = 0.836). The maximum reading speed was 174.2 ± 29.3 and 175.4 ± 27.8 in the right and left eye, respectively. Reading acuity was measured as logMAR 0.0 or better in 80% of the cases. All subjects showed a critical print size of 0.2 logMAR or better. The overall reading ability can be measured using the Korean version of the MNREAD acuity chart, thereby making it useful in measuring the reading ability of those with Korean as their native language.

Bridging the literacy gap for surgical consents: an AI-human expert collaborative approach

Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P < 0.05) following GPT-4-faciliated simplification. Readability improved from an average college freshman to an 8th-grade level (P = 0.004), matching the average American’s reading level. Medical and legal sufficiency consistency was confirmed. GPT-4 generated procedure-specific consent forms for five varied surgical procedures at an average 6th-grade reading level. These forms received perfect scores on a standardized consent form rubric and withstood scrutiny upon expert subspeciality surgeon review. This study demonstrates the first AI-human expert collaboration to enhance surgical consent forms, significantly improving readability without sacrificing clinical detail. Our framework could be extended to other patient communication materials, emphasizing clear communication and mitigating disparities related to health literacy barriers.

Potential Usefulness a Coronal View using an Automated Breast Ultrasound System in Detecting Breast Lesions.

An automated breast ultrasound system (ABUS) combined with screening mammography has increased cancer detection rates; however, supplemental ABUS use has increased recall rates. In this study, we aimed to identify an accurate and efficient method of ABUS interpretation and evaluate the potential usefulness of its coronal view versus the conventional transverse view. This retrospective observer study included comprised 114 ABUS cases (40 normal, 35 benign, 39 malignant). Ten physicians from multiple institutions interpreted the anonymized coronal and transverse views independently. The observers scored their confidence in the lesion detection for each case using a continuous scale and recorded reading times for each coronal and transverse view interpretation. Free-response receiver operating characteristic analysis was employed to compare detection accuracies between views; a paired t-test was used to compare the average reading times. Detection accuracy did not differ significantly between the coronal and transverse views (figure of merit=0.740 and 0.745, respectively; p = 0.72). However, the average reading time for the coronal view was significantly shorter than that for the transverse view (149.7 vs. 200.3 seconds per case, p = 0.003). The coronal view obtained with the ABUS was useful for interpretation and associated with significantly shorter reading times compared with the conventional transverse view while maintaining breast lesion detection accuracy.

RANCANG BANGUN SISTEM PRESENSI KULIAH MENGGUNAKAN QRCODE BERBASIS ESP32CAM

Rapid Response Codes are a kind of digital bar code that are becoming more and more popular since they can store a lot of data fast and in big quantities. This research aims to design a device for collecting attendance data using QR codes. This QR code contains information about the user's identity and location when used. We may determine the user's distance from the designated area using this location data. This location information is used to find out how far the user is from the area we have set.The presence method used the ESP Cam device to scan a QR code from the user which contains information on student identification number, date and location (latitude and longitude).Before scanning the QR code, users must confirm via the Telegram application which has been prepared in advance to carry out the attendance process at each class session.Ifthe information matches that is stored in the database and the user's location is still within the radius of the reader device, the user's identity data is then uploaded to the online presence system.After the hardware and software have been prepared, it continues with testing on Electrical Engineering students at Muhammadiyah University of Surakarta as attendance at the Electrical Engineering Laboratory.Scanning this QR code takes an average reading time of 3.5 seconds with the fastest estimated time being 3 seconds and the longest 5 seconds.

Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study.

The aim of this study was to evaluate the diagnostic performance of nonspecialist readers with and without the use of an artificial intelligence (AI) support tool to detect traumatic fractures on radiographs of the appendicular skeleton. The design was a retrospective, fully crossed multi-reader, multi-case study on a balanced dataset of patients (≥2 years of age) with an AI tool as a diagnostic intervention. Fifteen readers assessed 340 radiographic exams, with and without the AI tool in 2 different sessions and the time spent was automatically recorded. Reference standard was established by 3 consultant radiologists. Sensitivity, specificity, and false positives per patient were calculated. Patient-wise sensitivity increased from 72% to 80% (P < .05) and patient-wise specificity increased from 81% to 85% (P < .05) in exams aided by the AI tool compared to the unaided exams. The increase in sensitivity resulted in a relative reduction of missed fractures of 29%. The average rate of false positives per patient decreased from 0.16 to 0.14, corresponding to a relative reduction of 21%. There was no significant difference in average reading time spent per exam. The largest gain in fracture detection performance, with AI support, across all readers, was on nonobvious fractures with a significant increase in sensitivity of 11 percentage points (pp) (60%-71%). The diagnostic performance for detection of traumatic fractures on radiographs of the appendicular skeleton improved among nonspecialist readers tested AI fracture detection support tool showed an overall reader improvement in sensitivity and specificity when supported by an AI tool. Improvement was seen in both sensitivity and specificity without negatively affecting the interpretation time. The division and analysis of obvious and nonobvious fractures are novel in AI reader comparison studies like this.

Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs

To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and assess the performance of radiologists and emergency physicians in identifying fractures of the extremities with and without the DL aid. The DL tool was previously developed using 132,000 appendicular skeletal radiographs divided into 87% training, 11% validation, and 2% test sets. Stand-alone performance was evaluated on 2626 de-identified radiographs from a single institution in Ohio, including at least 140 exams per body region. Consensus from three US board-certified musculoskeletal (MSK)radiologists served as ground truth. A multi-reader retrospective study was performed in which 24 readers (eight each of emergency physicians, non-MSK radiologists, and MSK radiologists) identified fractures in 186 cases during two independent sessions with and without DLaid, separated by a one-month washout period. The accuracy (area under the receiver operating curve), sensitivity, specificity, and reading time werecompared withand without model aid. The model achieved a stand-alone accuracy of 0.986, sensitivity of 0.987, and specificity of 0.885, and high accuracy (> 0.95) across stratification for body part, age, gender, radiographic views, and scanner type. With DL aid, reader accuracy increased by 0.047 (95% CI: 0.034, 0.061; p=0.004) and sensitivity significantly improved from 0.865 (95% CI: 0.848, 0.881) to 0.955 (95% CI: 0.944, 0.964). Average reading time was shortened by 7.1s (27%) per exam. When stratified by physician type, this improvement was greater for emergency physicians and non-MSK radiologists. The DL tool demonstrated high stand-alone accuracy, aided physician diagnostic accuracy, and decreased interpretation time.

Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability.

Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (κ) and Student's t-test were used to assess the model and reader performance. The model achieved a κ of 0.87 (95% CI: 0.84, 0.89) for four-class density assessment and a κ of 0.91 (95% CI: 0.88, 0.93) for binary non-dense/dense assessment. Superiority tests showed significant reduction in inter-reader variability (κ improved from 0.70 to 0.88, p ≤ 0.001) and intra-reader variability (κ improved from 0.83 to 0.95, p ≤ 0.01) for four-class density, and significant reduction in inter-reader variability (κ improved from 0.77 to 0.96, p ≤ 0.001) and intra-reader variability (κ improved from 0.89 to 0.97, p ≤ 0.01) for binary non-dense/dense assessment when aided by DL. The average reader mean reading time per image pair also decreased by 30%, 0.86 s (95% CI: 0.01, 1.71), with six of seven readers having reading time reductions.

Lesion detection in digital breast tomosynthesis: human reader experiments indicate no benefit from the integration of information from multiple planes.

In digital breast tomosynthesis (DBT), radiologists need to review a stack of 20 to 80 tomosynthesis images, depending upon breast size. This causes a significant increase in reading time. However, it is currently unknown whether there is a perceptual benefit to viewing a mass in the 3D tomosynthesis volume. To answer this question, this study investigated whether adjacent lesion-containing planes provide additional information that aids lesion detection for DBT-like and breast CT-like (bCT) images. Human reader detection performance was determined for low-contrast targets shown in a single tomosynthesis image at the center of the target (2D) or shown in the entire tomosynthesis image stack (3D). Using simulations, targets embedded in simulated breast backgrounds, and images were generated using a DBT-like (50deg angular range) and a bCT-like (180deg angular range) imaging geometry. Experiments were conducted with spherical and capsule-shaped targets. Eleven readers reviewed 1600 images in two-alternative forced-choice experiments. The area under the receiver operating characteristic curve (AUC) and reading time were computed for the 2D and 3D reading modes for the DBT and bCT imaging geometries and for both target shapes. Spherical lesion detection was higher in 2D mode than in 3D, for both DBT- and bCT-like images (DBT: , , ; bCT: , , ), but equivalent for capsule-shaped signals (DBT: , , ; bCT: , , ). Average reading time was up to 134% higher for 3D viewing (). For the detection of low-contrast lesions, there is no inherent visual perception benefit to reviewing the entire DBT or bCT stack. The findings of this study could have implications for the development of 2D synthetic mammograms: a single synthesized 2D image designed to include all lesions present in the volume might allow readers to maintain detection performance at a significantly reduced reading time.

Yüz Felci Hakkında Bilgi Veren Türkçe Web Sitelerinin İçeriği, Okunabilirliği ve Güvenilirliği Üzerine Bir Değerlendirme

Background/Aims: Known as Bell's palsy, is a neurological disorder characterized by partial or complete paralysis of the muscles of the face, results from impairment or dysfunction of the facial nerve. This study aims to evaluate the readability, content, and quality and determine the level of accuracy and reliability of websites in Turkish language that provide information about facial paralysis. Methods: This descriptive, cross-sectional study analyzed the quality, usability, content and readability of Turkish facial paralysis websites using the Ateşman and Bezirci-Yılmaz Readability Formula, reading time, number of images, JAMA Benchmark Criteria, DISCERN Scale and UpToDate topics. Webscore.ai was used to assess aesthetic appeal and user experience. Results: After reviewing 200 websites, 110 Turkish sites about facial paralysis were included in the study. Average reading time was 5.5 minutes. Bezirci-Yılmaz score was 13.8. Ateşman readability levels were moderate. News, blog and "other" sites had the most visual content, followed by Health Professionals (HP) sites. Average quality of sites was poor. Conclusions: The DISCERN scale found 60% of Turkish health websites had poor-to-very poor content quality. UpToDate's benchmarking revealed 58.2% of sites contained less than half of the basic topics. This indicates inadequate, outdated info for facial paralysis patients. State Institutions and Health Related Associations should provide valid, reliable info on modern websites.

Reading or Pretending to Read? Analysis of the Behavior of Primary School Students during a Reading Comprehension Test

The article describes the analysis of students’ behavior when performing a computerized test of reading comprehension. On a sample of 2157 fourth graders in Krasnoyarsk, using the method of latent profile analysis, five typical profiles of students were identified based on combinations of variables: “average text reading time”, “number of quick answers”, “amount of time to read auxiliary text”, “total test time”. The identified groups of students are interpreted using the assessment results. The identified profiles will be useful for a deeper interpretation of the assessment results and as a practical basis for developing methods for a differential approach to teaching.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study.

Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography (CEM) images. In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system (MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion (AFF) algorithm that could intelligently incorporates multiple types of information from CEM images. The average free-response receiver operating characteristic score (AFROC-Score) was presented to validate system's detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases, comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists' performance. On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909 [95% confidence interval (95% CI): 0.822-0.996] and 0.912 (95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists' average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance. MDCS demonstrated excellent performance in the detection and classification of breast lesions, and greatly enhanced the overall performance of radiologists.