Alzheimer's Together with Mild Cognitive Impairment Screening Using Polar Transformation of Middle Zone of Fundus Images Based Deep Learning.

Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are considered an increasing major health problem in elderlies. However, current clinical methods of Alzheimer's detection are expensive and difficult to access, making the detection inconvenient and unsuitable for developing countries such as Thailand. Thus, we developed a method of AD together with MCI screening by fine-tuning a pre-trained Densely Connected Convolutional Network (DenseNet-121) model using the middle zone of polar transformed fundus image. The polar transformation in the middle zone of the fundus is a key factor helping the model to extract features more effectively and that enhances the model accuracy. The dataset was divided into 2 groups: normal and abnormal (AD and MCI). This method can classify between normal and abnormal patients with 96% accuracy, 99% sensitivity, 90% specificity, 95% precision, and 97% F1 score. Parts of both MCI and AD input images that most impact the classification score visualized by Grad-CAM++ focus in superior and inferior retinal quadrants.Clinical relevance- The parts of both MCI and AD input images that have the most impact the classification score (visualized by Grad-CAM++) are superior and inferior retinal quadrants. Polar transformation of the middle zone of retinal fundus images is a key factor that enhances the classification accuracy.

3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.

Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neural network (3-LbNets) to identify no glaucoma, glaucoma suspect, and glaucoma cases in global fundus images. 3-LbNets extracts important features from 3 different labeling modals and puts them into an artificial neural network (ANN) to find the final result. The method was effective, with an AUC of 98.66% for no glaucoma, 97.54% for glaucoma suspect, and 97.19% for glaucoma when analysing 206 fundus images evaluated with unanimous agreement from 3 well-trained ophthalmologists (3/3). When analysing 178 difficult to interpret fundus images (with majority agreement (2/3)), this method had an AUC of 80.80% for no glaucoma, 69.52% for glaucoma suspect, and 82.74% for glaucoma cases.Clinical relevance-This establishes a robust global fundus image screening network based on the ensemble method that can optimize glaucoma screening to alleviate the toll on those with glaucoma and prevent glaucoma suspects from developing the disease.

Donut: Augmentation Technique for Enhancing The Efficacy of Glaucoma Suspect Screening.

Glaucoma is the second most common cause of blindness. A glaucoma suspect has risk factors that increase the possibility of developing glaucoma. Evaluating a patient with suspected glaucoma is challenging. The "donut method" was developed in this study as an augmentation technique for obtaining high-quality fundus images for training ConvNeXt-Small model. Fundus images from GlauCUTU-DATA, labelled by randomizing at least 3 well-trained ophthalmologists (4 well-trained ophthalmologists in case of no majority agreement) with a unanimous agreement (3/3) and majority agreement (2/3), were used in the experiment. The experimental results from the proposed method showed the training model with the "donut method" increased the sensitivity of glaucoma suspects from 52.94% to 70.59% for the 3/3 data and increased the sensitivity of glaucoma suspects from 37.78% to 42.22% for the 2/3 data. This method enhanced the efficacy of classifying glaucoma suspects in both equalizing sensitivity and specificity sufficiently. Furthermore, three well-trained ophthalmologists agreed that the GradCAM++ heatmaps obtained from the training model using the proposed method highlighted the clinical criteria.Clinical relevance- The donut method for augmentation fundus images focuses on the optic nerve head region for enhancing efficacy of glaucoma suspect screening, and uses Grad-CAM++ to highlight the clinical criteria.

Extravasation Screening and Severity Prediction from Skin Lesion Image using Deep Neural Networks.

Extravasation occurs secondary to the leakage of medication from blood vessels into the surrounding tissue during intravenous administration resulting in significant soft tissue injury and necrosis. If treatment is delayed, invasive management such as surgical debridement, skin grafting, and even amputation may be required. Thus, it is imperative to develop a smartphone application for predicting extravasation severity from skin image. Two Deep Neural Network (DNN) architectures, U-Net and DenseNet-121, were used to segment skin and lesion, and to classify extravasation severity. Sensitivity and specificity for predicting between asymptomatic and abnormal cases were 77.78 and 90.24%. For each severity in abnormal cases, mild extravasation attained the highest F1-score of 0.8049, followed by severe extravasation of 0.6429, and moderate extravasation of 0.6250. The F1-score of moderate-to-severe extravasation classification can improve by applying the our proposed rule-based for multi-class classification. These findings proposed a novel and feasible DNN approach for screening extravasation from skin images. The implementation of DNN-based applications on mobile devices has a strong potential for clinical application in low-resource countries. Clinical relevance- The application can serve as a valuable tool in monitoring when extravasation occurs during intravaneous administration. It can also help in the scheduling process across worksite to reduce the risks associated with working shifts.

Classification of Depression and Other Psychiatric Conditions Using Speech Features Extracted from a Thai Psychiatric and Verbal Screening Test.

Depression is a common and serious mental illness which negatively affects daily functioning. To prevent the progression of the illness into severe or long-term consequences, early diagnosis is crucial. We developed an automated speech feature analysis application for depression and other psychiatric disorders derived from a developed Thai psychiatric and verbal screening test. The screening test includes Thai's version of Patient Health Questionnaire-9 (PHQ-9) and Hamilton Depression Rating Scale (HAM-D), and 32 additional emotion-induced questions. Case-control study was conducted on speech features from 66 participants. Twenty seven of those had depression (DP), 12 had other psychiatric disorders (OP), and 27 were normal controls (NC). The five-fold cross-validation from 6 settings of 5 classifiers with the combination of PHQ-9 and HAM-D scores, and speech features were examined. Results showed highest performance from the multilayer perceptron (MLP) classifier which yielded 83.33% sensitivity, 91.67% specificity, and 83.33% accuracy, where negative-emotional questions were most effective in classification. The automated speech feature analysis showed promising results for screening patients with depression or other psychiatric disorders. The current application is accessible through smartphone, making it a feasible and intuitive setup for low-resource countries such as Thailand.

Thammasat-NECTEC-Chula's Thai Language and Cognition Assessment (TLCA): The Thai Alzheimer's and Mild Cognitive Impairment Screening Test.

Thammasat-NECTEC-Chula's Thai Language and Cognition Assessment (TLCA) is a cognitive paper-based test consisting of 21 tasks that cover 3 domains: memory, language, and other cognitive abilities. The TLCA follows some aspects of the existing tests (Thai Addenbrooke's Cognitive Examination-Revised (Thai-ACE-R) and the Thai Montreal Cognitive Assessment Test (Thai-MoCA)) and many parts were reconstructed to be more adapted to the Thai culture. Data obtained from the test will be able to precisely distinguish between patients with Mild Cognitive Impairment (MCI), Alzheimer's Disease (AD), and Normal healthy Controls (NC). The TLCA was tested on 90 participants (32 on the paper-based version and 58 on the computerized version) using a scoring procedure and speech features from verbal responses with machine learning classification. The scoring results showed significant difference between non-AD (NC + MCI) vs AD participants in 3 domains and could differentiate between NC and MCI, while machine classification could classify in three settings: NC vs non-NC (MCI + AD), AD vs non-AD and NC vs MCI vs AD. These promising results suggest that TLCA could be further verified and used as an efficient assessment in MCI and AD screening for Thais.Clinical relevance- The speech feature analysis of TLCA showed promising result for screening MCI and AD for Thais.

Development of Thai Picture Description Task for Alzheimer's Screening using Part-of-Speech Tagging.

Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) are among the most common health conditions in elderly patients. Currently, methods to diagnose AD and MCI are lengthy, costly and require specialized staff to operate. A picture description task was developed to speed up the diagnosis. It was designed to be suitable and relatable to the Thai culture. In this paper, we will be presenting two picture description tasks named Thais-at-Home and Thai Temple Fair. The developed picture set was presented to 90 participants (30 normals, 30 MCI patients, and 30 AD patients). Then, the recording in the form of spontaneous speech is converted to text. A Part-of-Speech (PoS) tagger is used to categorize words into 7 types (noun, pronoun, adjective, verb, conjunction, preposition, and interjection) according to the Office of the Royal Society of Thailand. Six machine learning algorithms were applied to train with the PoS patterns and their performances were compared. Results showed that the PoS can be used to classify patients (MCI and AD) and healthy controls using multilayer perceptron with 90.00% sensitivity, 80.00% specificity, and 86.67% accuracy. Moreover, the findings showed that healthy controls used more conjunctions and verbs but fewer pronouns than the patients.Clinical relevance- The picture description tasks using part-of-speech (PoS) to showed promising results in screening Alzheimer's patients.

GlauCUTU: Virtual Reality Visual Field Test.

This study proposed a virtual reality (VR) head-mounted visual field (VF) test system, or also known as the GlauCUTU VF test, for a reaction time (RT) perimetry with moving visual stimuli that progressively increase in intensity. The test entailed 24-2 VF protocol and was examined on 2 study groups, controls with normal fields and subjects with glaucoma. To collect reaction times, participants were urged to respond to the stimulus by pressing on the clicker as fast as possible. Performance of the GlauCUTU VF test was compared to the gold standard Humphrey Visual Field Analyzer (HFA). The HFA showed a significant difference between the GlauCUTU and HFA with mean duration of 254.41 and 609, respectively [t(16) = 15.273, p<0.05]. Likewise, our system also effectively differentiated glaucomatous eyes from normal eyes for the left eye and right eye, respectively. When compared to the HFA, the GlauCUTU test produced a significantly shorter average test duration by 354 seconds which reduced test-induced eye fatigue. The portable and inexpensive GlauCUTU perimetry system proves to be a promising method for increasing accessibility to glaucoma screening.Clinical relevance- GlauCUTU, an automated head-mounted VR perimetry device for VF test, is portable, cost-effective, and suitable for low resource settings. Unlike the conventional HFA test, GlauCUTU VF test reports in terms of subjects RT which is reportedly higher in glaucoma patients.

Development of Computerized Tool for Screening Thai Children at Risk for Learning Disabilities.

A computerized version of "Noo-Khor-Arn" `May I read?', a paper-based screening test for Thai children at risk with Learning Disability (LD), was developed and some core ideas of development were given in details. Six test categories with 23 subtests were conducted on 110 Thai children aged between 7-12 years old (Mean = 7.94, SD = 1.45), divided into 50 LD and 60 Typically Developing (TD) children to determine most relevant test categories and subtests for classifying between the groups. Two-factor balanced Analysis of Variance (ANOVA) revealed that a computerized version shown a significant difference between TD and LD groups in the tasks related to linguistics, decoding, and naming. These tasks were Phonological Awareness (PA), Morphological Awareness (MA), Decoding (DEC), and Rapid Naming (RN), respectively. The rest of the test categories showed non-significant factors between TD and LD. Not only the results can be used for classification but also for streamlining the test categories and subtests, to shorten the test tool.Clinical relevance- The subtests related to linguistics and decoding aspects showed promising results in screening children at risk for learning disabilities.