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The knowledge-sharing, decision-making model can not only combine the two processes but also improve the teaching quality and students' learning achievements. However, the existing knowledge-sharing, decision-making model has many shortcomings. Therefore, for this article the authors applied the computer-aided instruction (CAI) system to the knowledge-sharing decision model and compared it with the traditional knowledge-sharing decision model. They compared and analyzed the application effects of the two processes in the student education management platform, and the experimental comparison was made from the perspective of curriculum and students. When the authors used the traditional knowledge-sharing decision model, the average score of the five people in the course A exam was 78.2. After they used the knowledge-sharing decision model based on the computer-aided instruction system, the average score of the five people in the course A exam was 85.4. Obviously, the knowledge-sharing, decision-making model based on the CAI system can effectively improve students' academic performances. [ABSTRACT FROM AUTHOR]

Abstract Background Osteoarthritis (OA) is common in aged adults and can result in muscle weakness and function limitations in lower limbs. Knee OA affects the quality of life in the elderly. Technology-supported feedback to achieve lower impact on knee joints and individualized exercise could benefit elderly patients with knee OA. Herein, a computer-aided feedback rowing exercise system is proposed, and its effects on improving muscle strength, health conditions, and knee functions of older adults with mild knee OA were investigated. Methods Thirty-eight older adults with mild knee OA and satisfying the American College of Rheumatology (ACR) clinical criteria participated in this randomized controlled clinical trial. Each subject was randomly assigned to a computer-aided rowing exercise (CRE) group (n = 20) or a control group (CON) (n = 18) that received regular resistance exercise programs two times per week for 12 weeks. Outcome measurements, including the Western Ontario and MacMaster Universities (WOMAC), muscle strength and functional fitness of the lower limbs, were evaluated before and after the intervention. Results Participants’ functional fitness in the CRE group exhibited significantly higher adjusted mean post-tests scores, including the WOMAC (p = 0.006), hip abductors strength (kg) (MD = 2.36 [1.28, 3.44], p = 5.67 × 10–5), hip adductors strength (MD = 3.04 [1.38, 4.69], p = 0.001), hip flexors strength (MD = 4.01 [2.24, 5.78], p = 6.46 × 10−5), hip extensors strength (MD = 2.88 [1.64, 4.12], p = 4.43 × 10−5), knee flexors strength (MD = 2.03 [0.66, 3.41], p = 0.005), knee extensors strength (MD = 1.80 [0.65, 2.94], p = 0.003), and functional-reach (cm) (MD = 3.74 [0.68, 6.80], p = 0.018), with large effect sizes (η2 = 0.17–0.42), than those in the CON group after the intervention. Conclusions Older adults with knee OA in the CRE group exhibited superior muscle strength, health conditions, and functional fitness improvements after the 12-week computer-aided rowing exercise program than those receiving the conventional exercise approach. Trial registration The Institutional Review Board of the Taipei Medical University approved the study protocol (no. N201908020, 27/05/2020) and retrospectively registered at ClinicalTrials.gov (trial registry no. NCT04919486, 09/06/2021).

Iron Deficiency Anemia (IDA) is the nutritional disorder that occurs when the body does not contain enough iron, an essential component of hemoglobin (Hb). The World Health Organization (WHO) estimated that IDA is the main cause of anemia in 1.62 billion cases worldwide [1]. Although IDA rarely results in death, it has significant adverse impacts on human health. During diagnosis, the hemoglobin indices show low mean corpuscular hemoglobin and mean corpuscular hemoglobin volume. On Peripheral Blood Smear (PBS) images viewed under a microscope by hematologists, IDA shows hypochromic and microcytic red cells. The purpose of the proposed research is to develop a computer-aided system that will allow hematologists to diagnose and detect diseases more accurately and quickly, therefore saving them time and effort. In order to diagnose or detect clinical disorders, non-invasive techniques like machine learning algorithms are employed. This work aims to identify IDA by utilizing the RetinaNet-Disentangled Dense Object Detector (DDOD) to localize hypochromic microcytes in PBS images. To the best of our knowledge, this is the first work using the object detection technique to detect IDA based on the Red Blood Cell (RBC) morphology. We carried out an extensive quantitative and qualitative evaluation of the model. Additionally, a comparison was made between the performance of our model and other object detection models. The results showed that our approach outperformed state-of-the-art techniques, with a mean average precision that was more than 8% higher., (Creative Commons Attribution license.)

Physicochemical and sensory analyses are commonly used to determine the quality characteristics of food samples in Food Industries. These methods are tedious, laborious, produce chemical residues, and involve the destruction of the samples. For the meat industries, this work proposes a non-invasive and non-destructive computer-aided inspection system, based on computer vision and ensemble machine learning techniques. The paper presents all the possibilities for the development of the system, making an exhaustive comparison of different algorithms used to extract features from the images of the samples, and various machine learning approaches, studying up to 6160 different models, and selecting the top 110 for the ensemble proposal. The system determines all the physicochemical, textural, and sensory quality characteristics of pork and beef loins in four meat states (fresh, thawed, cooked, and cured) with good precision, being a real alternative to the usual methods for the Food Industry.

Background: Osteoarthritis (OA) is common in aged adults and can result in muscle weakness and function limitations in lower limbs. Knee OA affects the quality of life in the elderly. Technology-supported feedback to achieve lower impact on knee joints and individualized exercise could benefit elderly patients with knee OA. Herein, a computer-aided feedback rowing exercise system is proposed, and its effects on improving muscle strength, health conditions, and knee functions of older adults with mild knee OA were investigated.Methods: Thirty-eight older adults with mild knee OA and satisfying the American College of Rheumatology (ACR) clinical criteria participated in this randomized controlled clinical trial. Each subject was randomly assigned to a computer-aided rowing exercise (CRE) group (n = 20) or a control group (CON) (n = 18) that received regular resistance exercise programs two times per week for 12 weeks. Outcome measurements, including the Western Ontario and MacMaster Universities (WOMAC), muscle strength and functional fitness of the lower limbs, were evaluated before and after the intervention.Results: Participants' functional fitness in the CRE group exhibited significantly higher adjusted mean post-tests scores, including the WOMAC (p = 0.006), hip abductors strength (kg) (MD = 2.36 [1.28, 3.44], p = 5.67 × 10-5), hip adductors strength (MD = 3.04 [1.38, 4.69], p = 0.001), hip flexors strength (MD = 4.01 [2.24, 5.78], p = 6.46 × 10-5), hip extensors strength (MD = 2.88 [1.64, 4.12], p = 4.43 × 10-5), knee flexors strength (MD = 2.03 [0.66, 3.41], p = 0.005), knee extensors strength (MD = 1.80 [0.65, 2.94], p = 0.003), and functional-reach (cm) (MD = 3.74 [0.68, 6.80], p = 0.018), with large effect sizes (η2 = 0.17-0.42), than those in the CON group after the intervention.Conclusions: Older adults with knee OA in the CRE group exhibited superior muscle strength, health conditions, and functional fitness improvements after the 12-week computer-aided rowing exercise program than those receiving the conventional exercise approach.Trial Registration: The Institutional Review Board of the Taipei Medical University approved the study protocol (no. N201908020, 27/05/2020) and retrospectively registered at ClinicalTrials.gov (trial registry no. NCT04919486, 09/06/2021). [ABSTRACT FROM AUTHOR]

Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444-454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world's second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations. [ABSTRACT FROM AUTHOR]

Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI. [ABSTRACT FROM AUTHOR]

Background: Classification of the type of calcaneal fracture on CT images is essential in driving treatment. However, human-based classification can be challenging due to anatomical complexities and CT image constraints. The use of computer-aided classification system in standard practice is additionally hindered by the availability of training images. The aims of this study is to 1) propose a deep learning network combined with data augmentation technique to classify calcaneal fractures on CT images into the Sanders system, and 2) assess the efficiency of such approach with differential training methods.Methods: In this study, the Principle component analysis (PCA) network was selected for the deep learning neural network architecture for its superior performance. CT calcaneal images were processed through PCA filters, binary hashing, and a block-wise histogram. The Augmentor pipeline including rotation, distortion, and flips was applied to generate artificial calcaneus fractured images. Two types of training approaches and five data sample sizes were investigated to evaluate the performance of the proposed system with and without data augmentation.Results: Compared to the original performance, use of augmented images during training improved network performance accuracy by almost twofold in classifying Sanders fracture types for all dataset sizes. A fivefold increase in the number of augmented training images improved network classification accuracy by 35%. The proposed deep CNN model achieved 72% accuracy in classifying CT calcaneal images into the four Sanders categories when trained with sufficient augmented artificial images.Conclusion: The proposed deep-learning algorithm coupled with data augmentation provides a feasible and efficient approach to the use of computer-aided system in assisting physicians in evaluating calcaneal fracture types. [ABSTRACT FROM AUTHOR]

IntroductionWe aimed to investigate whether 18F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS).Materials and methodsOn 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUVmax, histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables.ResultsFor ER expression, correlations were mainly observed with 18F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness_ER and uniformity_HISTO (ρ = −0.386, p = 0.017) and correlation_PR and entropy_GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUVmax, skewness_ER, kurtosis_ER, entropy_HISTO, and uniformity_HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness_ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness_ER, none of the parameters were independent predictors. Indeed, skewness_ER was significantly higher in youngest patients (ρ = −0.351, p = 0.031) and in clinical stage III tumors (p = 0.023).ConclusionA heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies.

IntroductionThis study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers.Methods303 patients who underwent thyroidectomy from October 2018 to July 2019 were retrospectively reviewed. The diagnostic performance of the senior radiologist, the junior radiologist, and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the size of thyroid nodules.ResultsIn total, 186 malignant thyroid nodules, and 179 benign thyroid nodules were included; 168 were papillary thyroid carcinoma (PTC), 7 were medullary thyroid carcinoma (MTC), 11 were follicular carcinoma (FTC), 127 were follicular adenoma (FA) and 52 were nodular goiters. The CAD system showed a comparable specificity as the senior radiologist (86.0% vs. 86.0%, p > 0.99), but a lower sensitivity and a lower area under the receiver operating characteristic (AUROC) curve (sensitivity: 71.5% vs. 95.2%, p < 0.001; AUROC: 0.788 vs. 0.906, p < 0.001). The CAD system improved the diagnostic sensitivities of both the senior and the junior radiologists (97.8% vs. 95.2%, p = 0.063; 88.2% vs. 75.3%, p < 0.001).ConclusionThe use of the CAD system using artificial intelligence is a potential tool to distinguish malignant thyroid nodules and is preferable to serve as a second opinion for less experienced radiologists to improve their diagnosis performance.

Introduction: We aimed to investigate whether 18F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS). Materials and methods: On 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUVmax, histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables. Results: For ER expression, correlations were mainly observed with 18F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness_ER and uniformity_HISTO (ρ = −0.386, p = 0.017) and correlation_PR and entropy_GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUVmax, skewness_ER, kurtosis_ER, entropy_HISTO, and uniformity_HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness_ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness_ER, none of the parameters were independent predictors. Indeed, skewness_ER was significantly higher in youngest patients (ρ = −0.351, p = 0.031) and in clinical stage III tumors (p = 0.023). Conclusion: A heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers. Methods: 303 patients who underwent thyroidectomy from October 2018 to July 2019 were retrospectively reviewed. The diagnostic performance of the senior radiologist, the junior radiologist, and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the size of thyroid nodules. Results: In total, 186 malignant thyroid nodules, and 179 benign thyroid nodules were included; 168 were papillary thyroid carcinoma (PTC), 7 were medullary thyroid carcinoma (MTC), 11 were follicular carcinoma (FTC), 127 were follicular adenoma (FA) and 52 were nodular goiters. The CAD system showed a comparable specificity as the senior radiologist (86.0% vs. 86.0%, p > 0.99), but a lower sensitivity and a lower area under the receiver operating characteristic (AUROC) curve (sensitivity: 71.5% vs. 95.2%, p < 0.001; AUROC: 0.788 vs. 0.906, p < 0.001). The CAD system improved the diagnostic sensitivities of both the senior and the junior radiologists (97.8% vs. 95.2%, p = 0.063; 88.2% vs. 75.3%, p < 0.001). Conclusion: The use of the CAD system using artificial intelligence is a potential tool to distinguish malignant thyroid nodules and is preferable to serve as a second opinion for less experienced radiologists to improve their diagnosis performance. [ABSTRACT FROM AUTHOR]

This paper addressed a data modelling and functionality evaluation of traceability system requirements and its implementation for small- to medium-sized enterprises (SMEs). The major idea is to convert the data structure and system behaviour arising from traceability requirement into modelling using data modelling techniques in a user-friendly and economical way. Then, based on a case study the paper expounds the methodology by implementing the model to a computerised traceability information system. The system can manipulate operation data as well as quality data dynamically to keep the traceability information up to date and useful. The solution responds to all traceability requirements of integrity, data collecting, linking, and guarantees different types of lists and reports in the support of quick decision making with minimal human efforts. [ABSTRACT FROM AUTHOR]

Environmental microbes are certainly present in our surroundings since they are essential to the growth and survival of human advancement. The detailed analysis of environmental microorganisms (EMs) is very important to recognize, understand and make use of microbes as well and prevent damage. Extracting the discriminatory features from a limited-size dataset is very challenging for a deep learning model and a pure transformer-based network cannot achieve good classification results on a limited-size dataset due to the lack of muti-scale features. In this study, a novel vision transformer-based deep neural network is proposed by integrating the transformer with CNN for the classification of EM using microscopic images. The proposed network EMViT-Net has three main modules: a transformer module, a CNN module and a multilayer perceptron module. The transformer model extracted multiscale features to generate more discriminatory information from the images. A new separable convolutional parameter-sharing attention (SCPSA) block is integrated with the CNN module in the core of EMViT-Net, which makes the model robust to capture the local and global features, and simultaneously reduces the computational complexity of the model. The data augmentation is performed to introduce the variability in the dataset and counter the problem of overfitting and data imbalance. After extensive experiments and detailed analysis, it has been determined that the proposed model EMViT-Net outperforms the other existing methods and achieves state-of-the-art results with an accuracy of 71.17% which proves the effectiveness of the model for the classification of environmental microbes. • A transformer-based EMViT network is proposed for classifying environmental microorganisms with microscopic images. • The model uses CNN and multi-layer perceptron models for classification. • A new block SCPSA is introduced for capturing local and long-distance dependency at once. • The method efficiently extracts discriminatory local and global features from a small microorganism image dataset. • The proposed model EMViT-Net outperforms other state-of-the-art methods with a classification accuracy of 71.17%. [ABSTRACT FROM AUTHOR]

Knee arthritis is a common joint disease that usually requires a total knee arthroplasty. There are multiple surgical variables that have a direct impact on the correct positioning of the implants, and an optimal combination of all these variables is the most challenging aspect of the procedure. Usually, preoperative planning using a computed tomography scan or magnetic resonance imaging helps the surgeon in deciding the most suitable resections to be made. This work is a proof of concept for a navigation system that supports the surgeon in following a preoperative plan. Existing solutions require costly sensors and special markers, fixed to the bones using additional incisions, which can interfere with the normal surgical flow. In contrast, the authors propose a computer-aided system that uses consumer RGB and depth cameras and do not require additional markers or tools to be tracked. They combine a deep learning approach for segmenting the bone surface with a recent registration algorithm for computing the pose of the navigation sensor with respect to the preoperative 3D model. Experimental validation using ex-vivo data shows that the method enables contactless pose estimation of the navigation sensor with the preoperative model, providing valuable information for guiding the surgeon during the medical procedure.

• Clusters of microcalcifications can be considered as early signs of breast cancer. • A three phases approach is proposed: preprocessing-detection-clustering. • Application of circular Hough Transform for microcalcification detection. • The proposed method reached a sensitivity of 91.78 Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians' work with an automatic tool. In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78 % with 2.87 false positives per image. Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues. [ABSTRACT FROM AUTHOR]

It is proved that the navigation system use for direction finding of cell phones will greatly expand the functionality and services of telephone networks mobile operators. The direction-finding system advantages and creation of automated system of direction finding cell phones proposed, which significantly expand the possibilities of services of operators of cellular communication in the cell using standard computer equipment operators themselves have been revelead. The developed system has a modular design, which allows modifying software packages, expanding the functionality. The procedure and algorithm of direction finding, allowing piecewise linear approximation of the graph of the displacement of each subscriber is outlined. The developed direction-finding system application for statistical information and optimization of urban public services processing, choice of speed, route and other activities improving the quality of human life is proposed.

Background: The presence of metastatic lymph nodes is a prognostic indicator for patients with thyroid carcinomas and is an important determinant of clinical decision making. However, evaluating neck lymph nodes requires experience and is labor- and time-intensive. Therefore, the development of a computer-aided diagnosis (CAD) system to identify and differentiate metastatic lymph nodes may be useful. Methods: From January 2008 to December 2016, we retrieved clinical records for 804 consecutive patients with 812 lymph nodes. The status of all lymph nodes was confirmed by fine-needle aspiration. The datasets were split into training (263 benign and 286 metastatic lymph nodes), validation (30 benign and 33 metastatic lymph nodes), and test (100 benign and 100 metastatic lymph nodes). Using the VGG-Class Activation Map model, we developed a CAD system to localize and differentiate the metastatic lymph nodes. We then evaluated the diagnostic performance of this CAD system in our test set. Results: In the test set, the accuracy, sensitivity, and specificity of our model for predicting lymph node malignancy were 83.0%, 79.5%, and 87.5%, respectively. The CAD system clearly detected the locations of the lymph nodes, which not only provided identifying data, but also demonstrated the basis of decisions. Conclusion: We developed a deep learning–based CAD system for the localization and differentiation of metastatic lymph nodes from thyroid cancer on ultrasound. This CAD system is highly sensitive and may be used as a screening tool; however, as it is relatively less specific, the screening results should be validated by experienced physicians. [ABSTRACT FROM AUTHOR]

Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features. Secondly, the supervised phase is where various multi-class machine learning (ML) classifiers are trained for erythema severity scoring. Finally, we compare the proposed system with two popular unsupervised feature extractor methods, namely: bag of visual words model (BoVWs) and AlexNet pretrained model. Root mean square error (RMSE) and F1 score are used as performance measures for the learned dictionaries and the trained ML models, respectively. A psoriasis image set consisting of 676 images, is used in this study. Experimental results demonstrate that the use of the proposed procedure can provide a setup where erythema scoring is accurate and consistent. Also, it is revealed that dictionaries with large number of atoms and small patch sizes yield the best representative erythema severity features. Further, random forest (RF) outperforms other classifiers with F1 score 0.71, followed by support vector machine (SVM) and boosting with 0.66 and 0.64 scores, respectively. Furthermore, the conducted comparative studies confirm the effectiveness of the proposed approach with improvement of 9% and 12% over BoVWs and AlexNet based features, respectively. [ABSTRACT FROM AUTHOR]

Background and objective Being bedridden long-term can cause deterioration in patients’ physiological function and performance, limiting daily activities and increasing the incidence of falls and other accidental injuries. Little research has been carried out in designing effective detecting systems to monitor the posture and status of bedridden patients and to provide accurate real-time feedback on posture. The purposes of this research were to develop a computer-aided system for real-time detection of physical activities in bed and to validate the system's validity and test–retest reliability in determining eight postures: motion leftward/rightward, turning over leftward/rightward, getting up leftward/rightward, and getting off the bed leftward/rightward. Methods The in-bed physical activity detecting system consists mainly of a clinical sickbed, signal amplifier, a data acquisition (DAQ) system, and operating software for computing and determining postural changes associated with four load cell sensing components. Thirty healthy subjects (15 males and 15 females, mean age = 27.8 ± 5.3 years) participated in the study. All subjects were asked to execute eight in-bed activities in a random order and to participate in an evaluation of the test–retest reliability of the results 14 days later. Spearman's rank correlation coefficient was used to compare the system's determinations of postural states with researchers’ recordings of postural changes. The test–retest reliability of the system's ability to determine postures was analyzed using the interclass correlation coefficient ICC(3,1). Results The system was found to exhibit high validity and accuracy (r = 0.928, p < 0.001; accuracy rate: 87.9%) in determining in-bed displacement, turning over, sitting up, and getting off the bed. The system was particularly accurate in detecting motion rightward (90%), turning over leftward (83%), sitting up leftward or rightward (87–93%), and getting off the bed (100%). The test–retest reliability ICC(3,1) value was 0.968 (p < 0.001). Conclusions The system developed in this study exhibits satisfactory validity and reliability in detecting changes in-bed body postures and can be beneficial in assisting caregivers and clinical nursing staff in detecting the in-bed physical activities of bedridden patients and in developing fall prevention warning systems. [ABSTRACT FROM AUTHOR]

This study presents data on the application of two different processing techniques of foot parameters and the comparison of their validity and reliability in adults. Twenty-four healthy participants volunteered took part in the study. Three digital scans were obtained from each participant by one experienced investigator. The foot parameters were: Clarke index, 'KY' index of Sztriter-Godunov, heel angle and Wejsflog index. The parameters were identified into two ways: manually using software SolidWorks and by a new computer-aided system. The results of the Spearman's rank correlation suggest a strong positive relationship between parameters obtained from the computer-aided system and manually counted (R > 0.9). The current study suggests that computer-aided system is a practical tool for foot type assessment in adults and could be recommended for both research and clinical applications. [ABSTRACT FROM AUTHOR]

Background: An initial clinical assessment is described of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intelligence (AI) for thyroid ultrasound, and its performance is evaluated in the diagnosis of malignant thyroid nodules and categorization of nodule characteristics. Methods: Patients with thyroid nodules with decisive diagnosis, whether benign or malignant, were consecutively enrolled from November 2015 to February 2016. An experienced radiologist reviewed the ultrasound image characteristics of the thyroid nodules, while another radiologist assessed the same thyroid nodules using the CAD system, providing ultrasound characteristics and a diagnosis of whether nodules were benign or malignant. The diagnostic performance and agreement of US characteristics between the experienced radiologist and the CAD system were compared. Results: In total, 102 thyroid nodules from 89 patients were included; 59 (57.8%) were benign and 43 (42.2%) were malignant. The CAD system showed a similar sensitivity as the experienced radiologist (90.7% vs. 88.4%, p > 0.99), but a lower specificity and a lower area under the receiver operating characteristic (AUROC) curve (specificity: 74.6% vs. 94.9%, p = 0.002; AUROC: 0.83 vs. 0.92, p = 0.021). Classifications of the ultrasound characteristics (composition, orientation, echogenicity, and spongiform) between radiologist and CAD system were in substantial agreement (κ = 0.659, 0.740, 0.733, and 0.658, respectively), while the margin showed a fair agreement (κ = 0.239). Conclusion: The sensitivity of the CAD system using AI for malignant thyroid nodules was as good as that of the experienced radiologist, while specificity and accuracy were lower than those of the experienced radiologist. The CAD system showed an acceptable agreement with the experienced radiologist for characterization of thyroid nodules. [ABSTRACT FROM AUTHOR]

Background: Mandibular bone on panoramic radiographs has been proven to be useful for identifying postmenopausal women with low skeletal bone mineral density. One of the important parts of mandibular bone is trabecular bone. Trabecular bone architecture is one of the factors that governs bone strength and may be categorized as a contributor to bone quality. Purpose: The purposes of this study were to develop a computer-aided system for measuring trabecular bone line strength on panoramic radiographs in identifying postmenopausal women with osteoporosis and to clarify the diagnostic efficacy of the system. Methods: Reduction and expansion of trabecular bone sample images using a two level Gaussian pyramid for removing noises and small segments were first introduced. Then, line strength at each pixel was calculated based on its existence on the trabecular bone with emphasizes line segment which has similar orientation with the root of tooth. The density was measured with respect to line strength of segment structure which has similar orientation with the root of tooth, either on the left and the right in the mandibular bone. Number of pixels in the line segment area was compared with a threshold value to determine whether normal or osteoporosis. Results: From experiment on 100 data, the accuracy of 88%, sensitivity of 92%, and specificity of 86.7% were achieved. Conclusion: The computer-aided system of trabecular bone analysis may be useful for detecting osteoporosis using panoramic radiographs.Latar belakang: Tulang mandibula pada panoramik radiografi telah banyak diteliti dan terbukti mampu digunakan untuk mengidentifikasi wanita pasca menopause dengan menggunakan bone mineral density rendah. Salah satu bagian tulang mandibula yang penting adalah tulang trabekula. Arsitektur tulang trabekula merupakan salah satu dari faktor-faktor yang mempengaruhi kekuatan tulang dan dapat digolongkan sebagai kontributor bagi kualitas tulang. Tujuan: Penelitian ini bertujuan untuk membangun sebuah sistem dengan bantuan komputer untuk mengukur kekuatan garis pada tulang trabekula dan menggunakannya untuk mendeteksi osteoporosis pada wanita postmenopause. Metode: Dilakukan sampling pada sebagian tulang mandibular yang menghasilkan sebuah sampel citra. Sampel citra ini selanjutnya diperbaiki dari derau (noise) dengan menggunakan piramida Gaussian dua level. Kekuatan garis pada tiap piksel dihitung berdasarkan orientasi segmen garis tulang trabekula yang sejajar dengan akar gigi. Setelah dilakukan binerisasi, luasan segmen yang dihasilkan dihitung dan dibandingkan dengan sebuah nilai ambang. Bila luasan melebihi nilai threshold maka dikategorikan sebagai normal. Sebaliknya bila luasan dibawah nilai threshold, dikategorikan sebagai osteoporosis. Hasil: Berdasarkan eksperimen terhadap 100 data, sistem mampu mencapai akurasi identifikasi sebesar 88%, sensitivitas 92%, dan spesifisitas 86,7%. Kesimpulan: Sistem analisa trabecular bone dengan bantuan komputer ini dapat digunakan oleh para dokter gigi untuk mendeteksi osteoporosis menggunakan panoramik radiografi.

Product customization is a trend in the current market-oriented manufacturing environment. However, deduction from customer requirements to design results and evaluation of design alternatives are still heavily reliant on the designer's experience and knowledge. To solve the problem of fuzziness and uncertainty of customer requirements in product configuration, an analysis method based on the grey rough model is presented. The customer requirements can be converted into technical characteristics effectively. In addition, an optimization decision model for product planning is established to help the enterprises select the key technical characteristics under the constraints of cost and time to serve the customer to maximal satisfaction. A new case retrieval approach that combines the self-organizing map and fuzzy similarity priority ratio method is proposed in case-based design. The self-organizing map can reduce the retrieval range and increase the retrieval efficiency, and the fuzzy similarity priority ratio method can evaluate the similarity of cases comprehensively. To ensure that the final case has the best overall performance, an evaluation method of similar cases based on grey correlation analysis is proposed to evaluate similar cases to select the most suitable case. Furthermore, a computer-aided system is developed using MATLAB GUI to assist the product configuration design. The actual example and result on an ETC series machine tool product show that the proposed method is effective, rapid and accurate in the process of product configuration. The proposed methodology provides a detailed instruction for the product configuration design oriented to customer requirements. [ABSTRACT FROM AUTHOR]

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In this paper, a computer-aided system for orbital prosthesis rehabilitation is introduced. With the system, a 3D model of the orbital prosthesis can be easily reconstructed from the CT image of a patient by referring to the normal eye of the patient, and the rehabilitation result by the model can be simulated before the surgery. This facilitates surgeons to design appropriate orbital prosthesis and improve rehabilitation esthetics. Based on the system, the preoperative surgery planning for orbital implant can also be made. This improves the reliability, safety and intuition of the rehabilitation surgery well. The system has been applied to clinical CT images of patients, and the experimental results show effectiveness and acceptability of the system in the clinic. [ABSTRACT FROM AUTHOR]

Commercial buildings, which are ubiquitous in metropolises, are typically equipped with a wide range of facilities. Satisfactory operation of the facilities necessitates provision of effective maintenance works in the long run. Since, fully computerized maintenance management systems are seldom adopted for facilities in commercial buildings, the dearth of proper maintenance data makes it difficult to investigate their maintenance performance. Reported in this paper is an examination of the maintenance data of a large commercial building, which were recorded semi-automatically by a computer-aided maintenance system. The maintenance works were found to be mainly corrective in nature, dominated by the electrical trade. Existing occupancy and business operation were influential to the issue time of the work orders. Benchmarks of response time, repair time, and downtime were established for evaluation and benchmarking purposes. Further works are needed to probe into the factors that affect maintenance performance, especially the effectiveness of maintenance manpower deployment. [ABSTRACT FROM AUTHOR]

The article reports on the algorithm for the designing of led driver on the basis of the use of computer-aided design systems. Topics discussed include radiator projection, calculation of light characteristics, and calculation of transformers. Also discussed are the design problems, light-emitted diode systems, and optimization.

Machinery procurement is carried out in both government and private sectors. One of the major criteria for the selection of vendors is their ability to meet the due-date of supply. Failure to meet the supply due-date will affect installation time, commissioning time, as well as start- up date negatively. The set target for the period will be difficult to meet and working overtime to recover the lost period will cause over-flogging of machine, increase in wear rate which can lead to the life-span reduction of the machine. To arrest this situation, a stochastic model was developed to predict machinery/equipment procurement flow time and set due-date for the supply of the machine by the vendor. The model utilized data on the procurement activities which were analysed based on optimistic, most likely and pessimistic time for each activity. The stochastic model used Project Evaluation and Review Techniques (PERT) for analysing these three time estimates from which expected time was predicted. The model accommodates procurement strategic decisions, flow chart development, data collection form, network analysis, activities paths determination and critical path identification. This research was able to develop a model for machinery procurement, develop computer software for implementing the model and validate the effectiveness of the model using an existing company as case study. The expected completion time is 45 days, with variance of 2 days while the probability of supplying the machine in not more than 2 days is 86%. This model will find application in small, medium and large scale industries in both developing and developed countries.

The article describes the lighting equipment design. The algorithmic procedures at all design stages are developed. [ABSTRACT FROM AUTHOR]

The article presents a study which developed a computer-aided clinical patient education system to provide the required individual nursing care for psychiatric patients. The study was conducted in Taiwan. In the study, the system was developed by integrating the databases for disease, medicine, and nursing knowledge, and the system's feasibility and usability were assessed by clinical nurses.

Machinery procurement is carried out in both government and private sectors. One of the major criteria for the selection of vendors is their ability to meet the due-date of supply. Failure to meet the supply due-date will affect installation time, commissioning time, as well as start- up date negatively. The set target for the period will be difficult to meet and working overtime to recover the lost period will cause over-flogging of machine, increase in wear rate which can lead to the life-span reduction of the machine. To arrest this situation, a stochastic model was developed to predict machinery/equipment procurement flow time and set due-date for the supply of the machine by the vendor. The model utilized data on the procurement activities which were analysed based on optimistic, most likely and pessimistic time for each activity. The stochastic model used Project Evaluation and Review Techniques (PERT) for analysing these three time estimates from which expected time was predicted. The model accommodates procurement strategic decisions, flow chart development, data collection form, network analysis, activities paths determination and critical path identification. This research was able to develop a model for machinery procurement, develop computer software for implementing the model and validate the effectiveness of the model using an existing company as case study. The expected completion time is 45 days, with variance of 2 days while the probability of supplying the machine in not more than 2 days is 86%. This model will find application in small, medium and large scale industries in both developing and developed countries. [ABSTRACT FROM AUTHOR]

Purpose: Population-based routine service screening has gained popularity following an era of randomized controlled trials. The evaluation of these service screening programs is subject to study design, data availability, and the precise data analysis for adjusting bias. We developed a computer-aided system that allows the evaluation of population-based service screening to unify these aspects and facilitate and guide the program assessor to efficiently perform an evaluation. Methods: This system underpins two experimental designs: the posttest-only non-equivalent design and the one-group pretest–posttest design and demonstrates the type of data required at both the population and individual levels. Three major analyses were developed that included a cumulative mortality analysis, survival analysis with lead-time adjustment, and self-selection bias adjustment. We used SAS AF software to develop a graphic interface system with a pull-down menu style. Results: We demonstrate the application of this system with data obtained from a Swedish population-based service screen and a population-based randomized controlled trial for the screening of breast, colorectal, and prostate cancer, and one service screening program for cervical cancer with Pap smears. The system provided automated descriptive results based on the various sources of available data and cumulative mortality curves corresponding to the study designs. The comparison of cumulative survival between clinically and screen-detected cases without a lead-time adjustment are also demonstrated. The intention-to-treat and noncompliance analysis with self-selection bias adjustments are also shown to assess the effectiveness of the population-based service screening program. Model validation was composed of a comparison between our adjusted self-selection bias estimates and the empirical results on effectiveness reported in the literature. Conclusions: We demonstrate a computer-aided system allowing the evaluation of population-based service screening programs with an adjustment for self-selection and lead-time bias. This is achieved by providing a tutorial guide from the study design to the data analysis, with bias adjustment. [Copyright &y& Elsevier]

Background Health policy makers are usually stranded by the complicated infrastructure and intensive computation related to economic evaluation. Objectives It is therefore valuable to develop a computer-aided tool to help health personnel to perform economic evaluation with ease. Method The infrastructure for economic evaluation was first designed. Markov process with micro-simulation was applied to model the disease natural history or lifetime sequale to project the effectiveness by comparing all possible decisions. All the essential elements of economic evaluation together with sensitivity analysis are encoded in this computer-aided software written with SAS Screen Control Language in user-defined menu style. Illustration Screening versus no screening for colorectal cancer was used as an example. Conclusion The computer-aided model for economic evaluation was developed in this study. It is anticipated that the flexibility and user-defined menu style facilitate the wide application of economic evaluation to health care intervention program. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives The disease progression of cancer and non-malignant chronic disease often involve a multi-state transition. However, estimation of parameters and prediction regarding the multi-state disease process are complex. This study aimed to develop an estimation and prediction system with a computer-assisted software using SAS/SCL as a platform to predict the risk of any outcome arising from the underlying multi-state process with or without the incorporation of individual characteristics. Method The computer-aided system is first constructed following the theoretical framework of stochastic process. The functions provided in this software include model specification, formulation of likelihood function, parameter estimation, model validation and model prediction. An example of breast cancer screening for a high-risk group in Taiwan was used to demonstrate the usefulness of this software. Results The natural history of breast cancer of a three-state disease process has been demonstrated. Two suspected risk factors, late age at first full-term pregnancy and obesity, were considered by the form of the proportional hazard model. Formulation of intensity matrix, likelihood function, assignment of initial values, and parameter constraint and estimation were successfully demonstrated in model specification. Model validation suggested a good fit of the constructed model. The application of model prediction enables one to project the effectiveness of organized screening by different inter-screening intervals from a policy level or from an individual basis. Conclusions A computer-aided estimation and prediction system for multi-state disease process was developed and demonstrated. This system can be applied to data with the property of multi-state transitions in association with events or disease. [ABSTRACT FROM AUTHOR]

Abstract: The most dangerous places in ships are their power plants. Particularly, they are very unsafe for operators carried out various necessary operation and maintenance activities. For this reason, ship machinery should be designed to ensure the maximum safety for its operators. It is a very difficult task. Therefore, it could not be solved by means of conventional design methods, which are used for design of uncomplicated technical equipment. One of the possible ways of solving this problem is to provide appropriate tools, which allow us to take the operator''s safety into account during a design process, especially at its early stages. A computer-aided system supporting design of safe ship power plants could be such a tool. This paper deals with developing process of a prototype of the computer-aided system for hazard zone identification in ship power plants. [Copyright &y& Elsevier]

The intricacy of predictive models associated with prognosis and risk classification of disease often discourages medical personnel who are interested in this field. The aim of this study was therefore to develop a computer-aided disease prediction model underpinning a step-by-step statistics-guided approach including five components: (1) data management; (2) exploratory analysis; (3) type of predictive model; (4) model verification; (5) interactive mode of disease prediction using SAS 8.02 Windows 2000 as a platform.The application of this system was illustrated by using data from the Swedish Two-County Trial on breast cancer screening. The effects of tumour size, node status, and histological grade on breast cancer death using logistic regression model or survival models were predicted. A total of 20 questions were designed to exemplify the usefulness of each component. We also evaluated the system using a controlled randomized trial. Times to finish the above 20 questions were used as endpoint to evaluate the performance of the current system. User satisfaction with the current system such as easy to use, the efficiency of risk prediction, and the reduction of barrier to predictive model was also evaluated.The intervention group not only performed more efficiently than the control group but also satisfied with this application software.The MD-DP-SOS system characterized by menu-driven style, comprehensiveness, accuracy and adequacy assessment, and interactive mode of disease prediction is helpful for medical personnel who are involved in disease prediction. [ABSTRACT FROM AUTHOR]

Moulding conditions design refers to parameter settings of moulding conditions such as melt temperature, mould temperature and injection time. They are important factors in plastic injection moulding design. The determination of these parameters is a highly skilled job and has largely been dependent on injection moulding engineers' experience and intuitiveness. In this paper, the authors present a flexible computer-aided system to assist the designers by using simulation-based technology. One of the most prominent characteristics of the system is that it allows the designers to specify their intended quality measuring criteria such as minimum cavity pressure and shear stress, a uniform distribution of cooling time, end-of-fill temperature and volumetric shrinkage. The system uses weighted criteria as an objective function to determine optimal moulding conditions when multiple criteria are specified. Moldflow simulation results are used to compare different designs. A two-step exhaustive search strategy (from coarse to refined) is used to search for a near-optimal design. [ABSTRACT FROM AUTHOR]

Thyroid scintigraphy is an important investigation for the clinical diagnosis of thyroid diseases. Thyroid diseases often present characteristic abnormal patterns in scintigraphic images. In this study, we developed an automated recognition model for thyroid uptake patterns. These patterns were classified into six categories, and they are diffusely increased, diffusely decreased, heterogeneous, focally increased, focally decreased, and normal. This study is the first report on such data for automated thyroid pattern interpretation to the best of our knowledge. A thyroid uptake pattern recognition network (TPRNet) was developed, using deep and handcrafted features of thyroid patterns to perform classification. The method can be trained using the traditional back-propagation algorithm. An in-house dataset that contains 4263 thyroid scintigraphic images was constructed to train and validate the TPRNet. Furthermore, an external test dataset that includes 1318 images was constructed to test the TPRNet. Experimental results show that the proposed method is effective for recognizing thyroid patterns in scintigraphic images. It outperforms compared methods, notably in the external test dataset, demonstrating the good generalization ability of the TPRNet. The proposed method is also compared against four physicians' judgments on recognizing thyroid patterns, resulting in a performance that is comparable to that of experienced doctors, showing that it could be used in clinical practice. [ABSTRACT FROM AUTHOR]

Early screening is an important means to reduce breast cancer mortality. In order to solve the problem of low breast cancer screening rates caused by limited medical resources in remote and impoverished areas, this paper designs a breast cancer screening system aided with portable ultrasound Clarius. The system automatically segments the tumor area of the B-ultrasound image on the mobile terminal and uses the ultrasound radio frequency data on the cloud server to automatically classify the benign and malignant tumors. Experimental results in this study show that the accuracy of breast tumor segmentation reaches 98%, and the accuracy of benign and malignant classification reaches 82%, and the system is accurate and reliable. The system is easy to set up and operate, which is convenient for patients in remote and poor areas to carry out early breast cancer screening. It is beneficial to objectively diagnose disease, and it is the first time for the domestic breast cancer auxiliary screening system on the mobile terminal.