Reading Comprehension and Processing Time When People With Aphasia Use Text-to-Speech Technology With Personalized Supports and Features

Background: Person-centered approaches promote consistent use of supportive technology and feelings of empowerment for people with disabilities. Feature personalization is an aspect of person-centered approaches that can affect the benefit people with aphasia (PWA) derive from using text-to-speech (TTS) technology as a reading support. Aims: This study's primary purpose was to compare the comprehension and processing time of PWA when performing TTS-supported reading with preferred settings for voice, speech output rate, highlighting type, and highlighting color versus unsupported reading. A secondary aim was to examine initial support and feature preference selections, preference changes following TTS exposure, and anticipated functional reading activities for utilizing TTS technology. Method and Procedure: Twenty PWA read passages either via written text or text combined with TTS output using personally selected supports and features. Participants answered comprehension questions, reevaluated their preference selections, and provided feedback both about feature selections and possible future TTS technology uses. Outcomes and Results: Comprehension accuracy did not vary significantly between reading conditions; however, processing time was significantly less in the TTS-supported condition, thus suggesting TTS support promoted greater reading speed without compromising comprehension. Most participants preferred the TTS condition and several anticipated benefits when reading lengthy and difficult materials. Alterations to initial settings were relatively rare. Conclusions: Personalizing TTS systems is relevant to person-centered interventions. Reading with desired TTS system supports and features promotes improved reading efficiency by PWA compared with reading without TTS support. Attending to client preferences is important when customizing and implementing TTS technology as a reading support.

TOSREC-FR : Normalisation franco-québécoise du Test of Silent Reading Efficiency and Comprehension pour la sixième année

Cette étude présente les résultats de la normalisation du Test of Silent Reading Efficiency and Comprehension (TOSREC) auprès d’élèves franco-québécois de la sixième année du primaire et détaille les résultats de la démarche de validation du TOSREC-FR. Le TOSREC-FR pour la sixième année est un outil de dépistage qui s’administre en trois minutes à une classe entière et qui permet d’apprécier les habiletés de compréhension en lecture des élèves. Le TOSREC-FR pour la 6eannée a été normalisé dans treize classes (de 77 à 249 élèves par questionnaire) de la province de Québec durant les années scolaires 2018-2019 et 2019-2020. La normalisation du TOSREC-FR permet de classer les élèves selon leur niveau en lecture et de dépister les élèves à risque de présenter des difficultés scolaires avant l’entrée au secondaire.

Study of Cognitive State Recognition and Assistive System for Overall Reading of Foreign Literature Based on Intelligent Sensors

In real life, the text is one of the main carrier forms of information, which carries human civilization, and spreads knowledge to people, and also promotes culture and records history; however, how to read more information in a limited time, that is, to improve reading efficiency, has become a problem to be solved by current technology. The purpose of this paper is to integrate the existing wearable device concept, combined with a wireless intelligent sensor system; design a wearable reading assistance system designed to facilitate the use of blind and partially sighted people, based on the study and comparison of existing text recognition products; improve their functions and implementation platform, combined with wireless network; and design a wearable device that can achieve foreign text recognition and reading cognitive state reading assistance thereby improving reading efficiency. This paper proposes a method to implement foreign text decoding under the embedded platform with relatively few resources and quickly completes image acquisition, binarization, and compressed storage through the bit and storage area and DMA (direct memory access) double buffering mechanism unique to the chip selected in this paper; proposes to use the connected boundary tracking algorithm to find foreign text locators, reducing a large number of floating-point operations; does not rotate the image, instead, the image is directly sampled at the current rotation angle, and then foreign text bitstream information is acquired to realize the decoding of foreign text under the embedded platform with relatively fewer resources.

A-142 A Correlation between Reading Fluency and Processing Speed in a Child Population

Objective The aim of this study was to determine whether a correlation exists between reading fluency in the WJ-IV ACH and processing speed in the WISC-V. Method The data for this study was derived from a large de-identified database. Participants (n = 90) included individuals who completed the Wechsler Intelligence Scale for Children Fifth Edition (WISC-V), which measures intellectual ability, and the Woodcock Johnson IV Tests of Achievement (WJ-IV ACH), which tests for reading, writing, and mathematic achievement. The participants consisted of 36.7% White, 20% Black, 31.1% Hispanic, and 12.2% Other. From the sample, 66.7% were male and 32.3% were female. All the participants were administered the WISC-V and WJ-IV ACH (mean age = 10.53, SD = 2.50; mean education = 4.6, SD = 2.47). Vocabulary was controlled for. Results The results indicated a significant correlation between the WJ-IV ACH Reading Fluency and the WISC-V Processing Speed Index r(87) = 0.326, p = 0.002. Conclusions Previous research has found that deficits in processing speed affect reading efficiency. Cognitive processes are affected even in children with ADHD who are able to decode words accurately. Processing speed, specifically Coding in the WISC-IV, was found to be significantly associated with verbal span and measures of working memory. Moreover, processing speed and working memory have been found to be significant predictors of oral reading fluency (Jacobson et al., 2011). The current findings confirm a correlation between processing speed and reading fluency in updated versions of the forementioned assessments. Future research should investigate the role of comorbid diagnosis found in the functioning of both processing speed and reading fluency.

OP08 Automatic detection of enteric ulcers and erosions in capsule endoscopy using a convolutional neural network

Background Capsule endoscopy (CE) is recommended for the diagnosis and assessment of disease extension in patients with suspected or known inflammatory bowel disease, particularly Crohn’s disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and their identification in CE is crucial for an accurate evaluation of disease severity. Nevertheless, reviewing CE images is a time-consuming task, and the risk of overlooking lesions is significant. Over the last decade, artificial intelligence (AI) has emerged as a mean for overcoming these pitfalls. Of all AI methods, convolutional neural networks (CNN), due to their complex multilayer architecture present the best results in image analysis. We aimed to develop a CNN for the automatic identification of ulcers and erosions in the small bowel mucosa. Methods A total of 1483 CE exams (PillCam SB3®) performed at a single centre between 2015–2020 were analyzed. From these exams, a sum of 11588 frames of the enteric mucosa were obtained, 3163 containing enteric ulcers and erosions, and the remaining containing normal mucosa or other findings. Ulcers and erosions were stratified according to the Saurin’s classification for bleeding potential: P1E – erosions with intermediate bleeding risk; P1U – ulcers with intermediate bleeding risk; P2U – ulcers with high bleeding risk. For automatic identification of these lesions, these images were inserted into an CNN model with transfer learning. Subsequently, the performance of the network assessed using an independent set of images. The output provided by the CNN was compared to the classification provided by a consensus of specialists (Figure 1). Results After optimizing the neural architecture of the algorithm, our model was able to automatically detect and distinguish ulcers and erosions (any bleeding potential) in the small intestine mucosa with an accuracy of 96.7%, precision of 95.9%, sensitivity of 91.7% and a specificity of 97.8% (Figure 1). The mean processing time for the validation dataset was 23 seconds (approximately 101 frames/second). Conclusion We developed and tested a deep learning model based on a CNN for the automatic detection of enteric ulcers and erosions in CE images. Our system revealed extraordinary performance marks. We believe that our study lays the foundation for the development and application of effective AI tools to clinical practice. These techniques should improve diagnostic accuracy and reading efficiency.

A GSM MODULE-BASED SMART ELECTRIC METER READER

The traditional and estimated billing system of electric energy consumed in most part of Sub-Saharan Africa has become a lingering issue to the electricity consumers. This has therefore necessitated the advent of smart electric meters. In this work, we propose a smart electric meter reader that provides an efficient and economically viable technique for measuring the consumption of electricity. This proposed method tends to solve many issues of the traditional reading system, such as reading efficiency, accuracy, and the elimination of human interface. Our proposed method, consisting of a GSM module, is used to wirelessly communicate the smart meter readings to the electricity provider and the consumer in form of a text message. The results obtained from the evaluation of this work show that our proposed method has improved the accuracy of the meter reading process for proper accountability.

Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a clinical evaluation

Objectives: To investigate the impact of deep learning (DL) on radiologists’ detection accuracy and reading efficiency of rib fractures on CT. Methods: Blunt chest trauma patients (n = 198) undergoing thin-slice CT were enrolled. Images were read by two radiologists (R1, R2) in three sessions: S1, unassisted reading; S2, assisted by DL as the concurrent reader; S3, DL as the second reader. The fractures detected by the readers and total reading time were documented. The reference standard for rib fractures was established by an expert panel. The sensitivity and false-positives per scan were calculated and compared among S1, S2, and S3. Results: The reference standard identified 865 fractures on 713 ribs (102 patients) The sensitivity of S1, S2, and S3 was 82.8, 88.9, and 88.7% for R1, and 83.9, 88.7, and 88.8% for R2, respectively. The sensitivity of S2 and S3 was significantly higher compared to S1 for both readers (all p < 0.05). The sensitivity between S2 and S3 did not differ significantly (both p > 0.9). The false-positive per scan had no difference between sessions for R1 (p = 0.24) but was lower for S2 and S3 than S1 for R2 (both p < 0.05). Reading time decreased by 36% (R1) and 34% (R2) in S2 compared to S1. Conclusions: Using DL as a concurrent reader can improve the detection accuracy and reading efficiency for rib fracture. Advances in knowledge: DL can be integrated into the radiology workflow to improve the accuracy and reading efficiency of CT rib fracture detection.