Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data.

The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests a novel, automatic and qualitative scoring method using mobile sensor data and deep learning algorithms: CNN, a convolutional network, U-Net, a convolutional network for biomedical image segmentation, and the MNIST (Modified National Institute of Standards and Technology) database. To obtain DeepC, a trained model for segmenting a contour image from a hand drawn clock image, U-Net was trained with 159 CDT hand-drawn images at 128 × 128 resolution, obtained via mCDT. To construct DeepH, a trained model for segmenting the hands in a clock image, U-Net was trained with the same 159 CDT 128 × 128 resolution images. For obtaining DeepN, a trained model for classifying the digit images from a hand drawn clock image, CNN was trained with the MNIST database. Using DeepC, DeepH and DeepN with the sensor data, parameters of contour (0-3 points), numbers (0-4 points), hands (0-5 points), and the center (0-1 points) were scored for a total of 13 points. From 219 subjects, performance testing was completed with images and sensor data obtained via mCDT. For an objective performance analysis, all the images were scored and crosschecked by two clinical experts in CDT scaling. Performance test analysis derived a sensitivity, specificity, accuracy and precision for the contour parameter of 89.33, 92.68, 89.95 and 98.15%, for the hands parameter of 80.21, 95.93, 89.04 and 93.90%, for the numbers parameter of 83.87, 95.31, 87.21 and 97.74%, and for the center parameter of 98.42, 86.21, 96.80 and 97.91%, respectively. From these results, the mCDT application and its scoring system provide utility in differentiating dementia disease subtypes, being valuable in clinical practice and for studies in the field.

Automatic, Qualitative Scoring of the Interlocking Pentagon Drawing Test (PDT) based on U-Net and Mobile Sensor Data.

We implemented a mobile phone application of the pentagon drawing test (PDT), called mPDT, with a novel, automatic, and qualitative scoring method for the application based on U-Net (a convolutional network for biomedical image segmentation) coupled with mobile sensor data obtained with the mPDT. For the scoring protocol, the U-Net was trained with 199 PDT hand-drawn images of 512ⅹ512 resolution obtained via the mPDT in order to generate a trained model, Deep5, for segmenting a drawn right or left pentagon. The U-Net was also trained with 199 images of 512ⅹ512 resolution to attain the trained model, DeepLock, for segmenting an interlocking figure. Here, the epochs were iterated until the accuracy was greater than 98% and saturated. The mobile senor data primarily consisted of x and y coordinates, timestamps, and touch-events of all the samples with a 20 ms sampling period. The velocities were then calculated using the primary sensor data. With Deep5, DeepLock, and the sensor data, four parameters were extracted. These included the number of angles (0-4 points), distance/intersection between the two drawn figures (0-4 points), closure/opening of the drawn figure contours (0-2 points), and tremors detected (0-1 points). The parameters gave a scaling of 11 points in total. The performance evaluation for the mPDT included 230 images from subjects and their associated sensor data. The results of the performance test indicated, respectively, a sensitivity, specificity, accuracy, and precision of 97.53%, 92.62%, 94.35%, and 87.78% for the number of angles parameter; 93.10%, 97.90%, 96.09%, and 96.43% for the distance/intersection parameter; 94.03%, 90.63%, 92.61%, and 93.33% for the closure/opening parameter; and 100.00%, 100.00%, 100.00%, and 100.00% for the detected tremor parameter. These results suggest that the mPDT is very robust in differentiating dementia disease subtypes and is able to contribute to clinical practice and field studies.

Gut microbiota-based machine-learning signature for the diagnosis of alcohol-associated and metabolic dysfunction-associated steatotic liver disease.

Alcoholic-associated liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) show a high prevalence rate worldwide. As gut microbiota represents current state of ALD and MASLD via gut-liver axis, typical characteristics of gut microbiota can be used as a potential diagnostic marker in ALD and MASLD. Machine learning (ML) algorithms improve diagnostic performance in various diseases. Using gut microbiota-based ML algorithms, we evaluated the diagnostic index for ALD and MASLD. Fecal 16S rRNA sequencing data of 263 ALD (control, elevated liver enzyme [ELE], cirrhosis, and hepatocellular carcinoma [HCC]) and 201 MASLD (control and ELE) subjects were collected. For external validation, 126 ALD and 84 MASLD subjects were recruited. Four supervised ML algorithms (support vector machine, random forest, multilevel perceptron, and convolutional neural network) were used for classification with 20, 40, 60, and 80 features, in which three nonsupervised ML algorithms (independent component analysis, principal component analysis, linear discriminant analysis, and random projection) were used for feature reduction. A total of 52 combinations of ML algorithms for each pair of subgroups were performed with 60 hyperparameter variations and Stratified ShuffleSplit tenfold cross validation. The ML models of the convolutional neural network combined with principal component analysis achieved areas under the receiver operating characteristic curve (AUCs) > 0.90. In ALD, the diagnostic AUC values of the ML strategy (vs. control) were 0.94, 0.97, and 0.96 for ELE, cirrhosis, and liver cancer, respectively. The AUC value (vs. control) for MASLD (ELE) was 0.93. In the external validation, the AUC values of ALD and MASLD (vs control) were > 0.90 and 0.88, respectively. The gut microbiota-based ML strategy can be used for the diagnosis of ALD and MASLD.ClinicalTrials.gov NCT04339725.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.

In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.

Tribological characteristics of corrugated nano-scale dimpled and nanostructured surfaces.

The effects of ultrasonic nanocrystalline surface modification (UNSM) on the tribological characteristics of two different Cu-based alloys sintered on low carbon steel were investigated using a ball-on-disk reciprocating tribometer with a hardened bearing steel ball under oil-lubricated conditions. Experimental results showed that both the UNSM-treated Cu-based alloy specimens reduced the friction coefficient and enhanced the wear resistance compared to those of the polished specimens. Improvements in tribological characteristics of the UNSM-treated specimens may be attributed to the corrugated nano-scale dimpled and nanostructured surfaces and increased hardness. Addition of the 0.52% ferrum to Cu-based alloy is found to be beneficial in improving the tribological characteristics and in reducing the grain size. Scanning electron microscopy (SEM) was utilized to analyze the worn surfaces and characterize the wear mechanisms of the polished and UNSM-treated specimens. SEM analyses showed that the UNSM could reduce the abrasive wear which was the dominant wear mechanism of both Cu-based alloys specimens. In addition, the density and porosity measurement of both sintered Cu-based alloys revealed that the density increased and the porosity decreased after UNSM.

Relationship of Neural Correlates of Gait Characteristics and Cognitive Dysfunction in Patients with Mild Cognitive Impairment.

Background: Some patients with mild cognitive impairment (MCI) experience gait disturbances. However, there are few reports on the relationship between gait disturbance and cognitive function in patients with MCI. Therefore, we investigated the neural correlates of gait characteristics related to cognitive dysfunction. Methods: Eighty patients diagnosed with MCI from three dementia centers in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of 0.5 or higher, with a memory domain score of 0.5 or greater. The patients were classified as having either higher or lower MMSE and the groups were based on their Mini Mental Status Examination z-scores. Multiple logistic regression analysis was performed to examine the association between the gait characteristics and cognitive impairment. Analyses included variables such as age, sex, years of education, number of comorbidities, body mass index, and height. Results: Gait velocity, step count, step length, heel-to-heel base support, swing and stance phase duration, and support time were associated with cognitive function. A decrease in gray matter volume in the right pericalcarine area was associated with gait characteristics related to cognitive dysfunction. An increase in the curvature of gray matter in the right entorhinal, right lateral orbitofrontal, right cuneus, and right and left pars opercularis areas was also associated with gait characteristics related to cognitive dysfunction. Conclusion: Since gait impairment is an important factor in determining activities of daily living in patients with mild cognitive impairment, the evaluation of gait and cognitive functions in patients with mild cognitive impairment is important.

PM2.5 Forecast in Korea using the Long Short-Term Memory (LSTM) Model.

The National Institute of Environmental Research, under the Ministry of Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of particulate matter with diameters of ≤ 2.5 µm (PM2.5) in terms of four grades (low, moderate, high, and very high) over 19 districts nationwide. Particulate grades are subjectively designated by human forecasters based on forecast results from the Community Multiscale Air Quality (CMAQ) and artificial intelligence (AI) models in conjunction with weather patterns. This study evaluates forecasts from the long short-term memory (LSTM) algorithm relative to those from CMAQ-solely and AirKorea using observations from 2019. The skills of the one-day PM2.5 forecasts over the 19 districts were 39-70% for CMAQ, 72-79% for LSTM, and 73-80% for AirKorea; the AI forecasts showed comparable skills to the human forecasters at AirKorea. The one-day forecast skill levels of high and very high PM2.5 pollution grades are 31-98%, 31-74%, and 39-81% for the CMAQ-solely, the LSTM, and the AirKorea forecasts, respectively. Despite good skills for forecasting the high and very high events, CMAQ-solely forecasts also generate substantially higher false alarm rates (up to 86%) than the LSTM and AirKorea forecasts (up to 58%). Hence, applying only the LSTM model to the CMAQ forecasts can yield reasonable forecast skill levels comparable to the operational AirKorea forecasts that elaborately combine the CMAQ model, AI models, and human forecasters. The present results suggest that applications of appropriate AI models can greatly enhance PM2.5 forecast skills for Korea in a more objective way. Supplementary Information: The online version contains supplementary material available at 10.1007/s13143-022-00293-2.

The evaluation of the micro-tracks and micro-dimples on the tribological characteristics of thrust ball bearings.

One of the primary remedies for tribological problems is surface modification. The reduction of the friction between the ball and the raceway of bearings is a very important goal of the development of bearing technology. A low friction has a positive effect in terms of the extension of the fatigue life, avoidance of a temperature rise, and prevention of premature failure of bearings. Therefore, this research sought to investigate the effects of micro-tracks and micro-dimples on the tribological characteristics at the contact point between the ball and the raceway of thrust ball bearings (TBBs). The ultrasonic nanocrystal surface modification (UNSM) technology was applied using different intervals (feed rates) to the TBB raceway surface to create micro-tracks and micro-dimples. The friction coefficient after UNSM at 50 microm intervals showed marked sensitivity and a significant reduction of 30%. In this study, the results showed that more micro-dimples yield a lower friction coefficient.

The effect of ultrasonic nanocrystalline surface modification on the high-frequency fretting wear behavior of AISI304 steel.

The fact that one of fundamental characteristics of fretting is the very small sliding amplitude dictates the unique feature of wear mechanism. Ultrasonic Nanocrystalline Surface Modification (UNSM) technology was applied in order to investigate its effect on the high-frequency fretting wear behavior of AISI304 steel. Its influence on the fretting wear is also reported in this paper with these treated and untreated samples. UNSM delivers force onto the workpiece surface 20,000 times per second with 1,000 to 4,000 contact counts per square millimeter. UNSM creates homogenous nanocrystalline structures as well on the surface. UNSM process is expected to eliminate or significantly retard the formation of fretting wear. Nanocrystalline structure generation after UNSM has been reported to produce its unique structure and to offer a variety of beneficial properties compared to conventionally treated materials. A deformed layer of 220 microm exhibits high dislocation density, where top layer transformed to a nanostructure of the grain size in 23 nm and mechanical twins were observed. Deformation-induced martensite was observed to form at the intersections of mechanical twins, whose volume fraction has increased up to 38.4% and wear loss rate at 800,000 cycles has decreased by 40%. In this paper, experimental results are discussed to elucidate potential mechanism of high-frequency fretting wear.

Wear behavior of Cu-Zn alloy by ultrasonic nanocrystalline surface modification.

The ultrasonic nanocrystalline surface modification (UNSM) was applied to disk specimens made of Cu-Zn alloy in order to investigate the UNSM effects under five various conditions on wear of deformation twinning. In this paper, ball-on-disk test was conducted, and the results of UNSM-treated specimens showed that surface layer dislocation density and multi-directional twins were abruptly increased, and the grain size was altered into nano scale. UNSM delivers force onto the workpiece surface 20,000 times per second with 1,000 to 4,000 contact counts per square millimeter. The UNSM technology creates nanocrystalline and deformation twinning on the workpiece surface. One of the main concepts of this study is that defined phenomena of the UNSM technology, and the results revealed that nanocrystalline and deformation twinning depth might be controlled by means of impact energy of UNSM technology. EBSD and TEM analyses showed that deformation layer was increased up to 268 microm, and initial twin density was 0.001 x 10(6) cm(-2) and increased up to 0.343 x 10(6) cm(-2). Wear volume loss was also decreased from 703 x 10(3) mm3 to 387 x 10(3) mm3. Wear behavior according to deformation depth was observed under three different combinations. This is related to deformation depth which was created by UNSM technology.

Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment.

BACKGROUND: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer's disease. METHODS: We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made. RESULTS: The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65-0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649). CONCLUSIONS: Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.

Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors.

Scratching is a main behavioral response accompanied by acute and chronic itch conditions, and has been quantified as an objective correlate to assess itch in studies using laboratory animals. Scratching has been counted mostly by human annotators, which is a time-consuming and laborious process. It has been attempted to develop automated scoring methods using various strategies, but they often require specialized equipment, costly software, or implantation of device which may disturb animal behaviors. To complement limitations of those methods, we have adapted machine learning-based strategy to develop a novel automated and real-time method detecting mouse scratching from experimental movies captured using monochrome cameras such as a webcam. Scratching is identified by characteristic changes in pixels, body position, and body size by frame as well as the size of body. To build a training model, a novel two-step J48 decision tree-inducing algorithm along with a C4.5 post-pruning algorithm was applied to three 30-min video recordings in which a mouse exhibits scratching following an intradermal injection of a pruritogen, and the resultant frames were then used for the next round of training. The trained method exhibited, on average, a sensitivity and specificity of 95.19% and 92.96%, respectively, in a performance test with five new recordings. This result suggests that it can be used as a non-invasive, automated and objective tool to measure mouse scratching from video recordings captured in general experimental settings, permitting rapid and accurate analysis of scratching for preclinical studies and high throughput drug screening.