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  • 1
    Online Resource
    Online Resource
    A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema
    MDPI AG ; 2018
    In:  Journal of Clinical Medicine Vol. 7, No. 12 ( 2018-11-24), p. 475-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 7, No. 12 ( 2018-11-24), p. 475-
    Abstract: Purpose: Artificial neural networks (ANNs) are one type of artificial intelligence. Here, we use an ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema. Methods: Patient data were used to optimize ANNs for regression calculation. The target was established as the final visual acuity at 52, 78, or 104 weeks. The input baseline variables were sex, age, diabetes type or condition, systemic diseases, eye status and treatment time tables. Three groups were randomly devised to build, test and demonstrate the accuracy of the algorithms. Results: At 52, 78 and 104 weeks, 512, 483 and 464 eyes were included, respectively. For the training group, testing group and validation group, the respective correlation coefficients were 0.75, 0.77 and 0.70 (52 weeks); 0.79, 0.80 and 0.55 (78 weeks); and 0.83, 0.47 and 0.81 (104 weeks), while the mean standard errors of final visual acuity were 6.50, 6.11 and 6.40 (52 weeks); 5.91, 5.83 and 7.59; (78 weeks); and 5.39, 8.70 and 6.81 (104 weeks). Conclusions: Machine learning had good correlation coefficients for predicating prognosis with ranibizumab with just baseline characteristics. These models could be the useful clinical tools for prediction of success of the treatments.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm7120475
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2662592-1
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  • 2
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    Rapid Polyp Classification in Colonoscopy Using Textural and Convolutional Features
    MDPI AG ; 2022
    In:  Healthcare Vol. 10, No. 8 ( 2022-08-08), p. 1494-
    Details
    In: Healthcare, MDPI AG, Vol. 10, No. 8 ( 2022-08-08), p. 1494-
    Abstract: Colorectal cancer is the leading cause of cancer-associated morbidity and mortality worldwide. One of the causes of developing colorectal cancer is untreated colon adenomatous polyps. Clinically, polyps are detected in colonoscopy and the malignancies are determined according to the biopsy. To provide a quick and objective assessment to gastroenterologists, this study proposed a quantitative polyp classification via various image features in colonoscopy. The collected image database was composed of 1991 images including 1053 hyperplastic polyps and 938 adenomatous polyps and adenocarcinomas. From each image, textural features were extracted and combined in machine learning classifiers and machine-generated features were automatically selected in deep convolutional neural networks (DCNN). The DCNNs included AlexNet, Inception-V3, ResNet-101, and DenseNet-201. AlexNet trained from scratch achieved the best performance of 96.4% accuracy which is better than transfer learning and textural features. Using the prediction models, the malignancy level of polyps can be evaluated during a colonoscopy to provide a rapid treatment plan.
    Type of Medium: Online Resource
    ISSN: 2227-9032
    URL: Article
    DOI: 10.3390/healthcare10081494
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2721009-1
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  • 3
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    Computer-Aided Detection of Hyperacute Stroke Based on Relative Radiomic Patterns in Computed Tomography
    MDPI AG ; 2019
    In:  Applied Sciences Vol. 9, No. 8 ( 2019-04-23), p. 1668-
    Details
    In: Applied Sciences, MDPI AG, Vol. 9, No. 8 ( 2019-04-23), p. 1668-
    Abstract: Ischemic stroke is one of the leading causes of disability and death. To achieve timely assessments, a computer-aided diagnosis (CAD) system was proposed to perform early recognition of hyperacute ischemic stroke based on non-contrast computed tomography (NCCT). In total, 26 patients with hyperacute ischemic stroke (with onset 〈 6 h previous) and 56 normal controls composed the image database. For each NCCT slice, textural features were extracted from Ranklet-transformed images which had enhanced local contrast. Textural differences between the two sides of an image were calculated and combined in a machine learning classifier to detect stroke areas. The proposed CAD system using Ranklet features achieved significantly higher accuracy (81% vs. 71%), specificity (90% vs. 79%), and area under the curve (Az) (0.81 vs. 0.73) than conventional textural features. Diagnostic suggestions provided by the CAD system are fast and promising and could be useful in the pipeline of hyperacute ischemic stroke assessments.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app9081668
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2704225-X
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  • 4
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    Computer-Aided Bacillus Detection in Whole-Slide Pathological Images Using a Deep Convolutional Neural Network
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 12 ( 2020-06-12), p. 4059-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 12 ( 2020-06-12), p. 4059-
    Abstract: Mycobacterial infections continue to greatly affect global health and result in challenging histopathological examinations using digital whole-slide images (WSIs), histopathological methods could be made more convenient. However, screening for stained bacilli is a highly laborious task for pathologists due to the microscopic and inconsistent appearance of bacilli. This study proposed a computer-aided detection (CAD) system based on deep learning to automatically detect acid-fast stained mycobacteria. A total of 613 bacillus-positive image blocks and 1202 negative image blocks were cropped from WSIs (at approximately 20 × 20 pixels) and divided into training and testing samples of bacillus images. After randomly selecting 80% of the samples as the training set and the remaining 20% of samples as the testing set, a transfer learning mechanism based on a deep convolutional neural network (DCNN) was applied with a pretrained AlexNet to the target bacillus image blocks. The transferred DCNN model generated the probability that each image block contained a bacillus. A probability higher than 0.5 was regarded as positive for a bacillus. Consequently, the DCNN model achieved an accuracy of 95.3%, a sensitivity of 93.5%, and a specificity of 96.3%. For samples without color information, the performances were an accuracy of 73.8%, a sensitivity of 70.7%, and a specificity of 75.4%. The proposed DCNN model successfully distinguished bacilli from other tissues with promising accuracy. Meanwhile, the contribution of color information was revealed. This information will be helpful for pathologists to establish a more efficient diagnostic procedure.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10124059
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 5
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    Online Resource
    Near Field Sensing Applications with Tunable Beam Millimeter Wave Antenna Sensors in an All-in-One Chip Design
    MDPI AG ; 2022
    In:  Electronics Vol. 11, No. 14 ( 2022-07-17), p. 2231-
    Details
    In: Electronics, MDPI AG, Vol. 11, No. 14 ( 2022-07-17), p. 2231-
    Abstract: In this paper, a single-band beam control antenna is designed with a parallel coupler to realize a microstrip patch antenna passive wireless sensor in the form of a chip. It has a phase shift characteristic of the antenna radiation direction in the positive and negative directions. The antenna includes an orthogonal direction coupler design with a 90° parallel coupler in phase using a special structure that allows the whole chip area to be miniaturized while allowing the main beam angle to have a directivity function. The coupler is designed for the 28 GHz millimeter wave band. After feeding the patch antenna at the output port of the coupler and simultaneously feeding the excitation at the input port, the beam phase changes to +45° and +135° with a phase difference of 90°. The designed antenna size is 1160 μm × 790 μm, and the overall IC size is 1.2 mm × 1.2 mm. The power density simulation shows that the maximum power density is only 0.00797 W/kg for a 1 cm2 human sampling area, which means that the antenna sensor is suitable for use on human surfaces.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    URL: Article
    DOI: 10.3390/electronics11142231
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662127-7
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  • 6
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    Quantitative Analysis of Melanosis Coli Colonic Mucosa Using Textural Patterns
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 1 ( 2020-01-05), p. 404-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 1 ( 2020-01-05), p. 404-
    Abstract: Melanosis coli (MC) is a disease related to long-term use of anthranoid laxative agents. Patients with clinical constipation or obesity are more likely to use these drugs for long periods. Moreover, patients with MC are more likely to develop polyps, particularly adenomatous polyps. Adenomatous polyps can transform to colorectal cancer. Recognizing multiple polyps from MC is challenging due to their heterogeneity. Therefore, this study proposed a quantitative assessment of MC colonic mucosa with texture patterns. In total, the MC colonoscopy images of 1092 person-times were included in this study. At the beginning, the correlations among carcinoembryonic antigens, polyp texture, and pathology were analyzed. Then, 181 patients with MC were extracted for further analysis while patients having unclear images were excluded. By gray-level co-occurrence matrix, texture patterns in the colorectal images were extracted. Pearson correlation analysis indicated five texture features were significantly correlated with pathological results (p 〈 0.001). This result should be used in the future to design an instant help software to help the physician. The information of colonoscopy and image analystic data can provide clinicians with suggestions for assessing patients with MC.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10010404
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 7
    Online Resource
    Online Resource
    Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 22 ( 2021-11-18), p. 5787-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 22 ( 2021-11-18), p. 5787-
    Abstract: Gastrointestinal stromal tumors (GIST) are common mesenchymal tumors, and their effective treatment depends upon the mutational subtype of the KIT/PDGFRA genes. We established deep convolutional neural network (DCNN) models to rapidly predict drug-sensitive mutation subtypes from images of pathological tissue. A total of 5153 pathological images of 365 different GISTs from three different laboratories were collected and divided into training and validation sets. A transfer learning mechanism based on DCNN was used with four different network architectures, to identify cases with drug-sensitive mutations. The accuracy ranged from 87% to 75%. Cross-institutional inconsistency, however, was observed. Using gray-scale images resulted in a 7% drop in accuracy (accuracy 80%, sensitivity 87%, specificity 73%). Using images containing only nuclei (accuracy 81%, sensitivity 87%, specificity 73%) or cytoplasm (accuracy 79%, sensitivity 88%, specificity 67%) produced 6% and 8% drops in accuracy rate, respectively, suggesting buffering effects across subcellular components in DCNN interpretation. The proposed DCNN model successfully inferred cases with drug-sensitive mutations with high accuracy. The contribution of image color and subcellular components was also revealed. These results will help to generate a cheaper and quicker screening method for tumor gene testing.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13225787
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2527080-1
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  • 8
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    Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radiomic Features
    MDPI AG ; 2019
    In:  Applied Sciences Vol. 9, No. 22 ( 2019-11-16), p. 4926-
    Details
    In: Applied Sciences, MDPI AG, Vol. 9, No. 22 ( 2019-11-16), p. 4926-
    Abstract: According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor’s malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of ±1% and an area under the receiver operation characteristics curve (Az) of 0.9991 ± 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation ±16% and a mean Az of 0.7896 ± 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app9224926
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2704225-X
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  • 9
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    Online Resource
    Factors Associated with Significant Platelet Count Improvement in Thrombocytopenic Chronic Hepatitis C Patients Receiving Direct-Acting Antivirals
    MDPI AG ; 2022
    In:  Viruses Vol. 14, No. 2 ( 2022-02-07), p. 333-
    Details
    In: Viruses, MDPI AG, Vol. 14, No. 2 ( 2022-02-07), p. 333-
    Abstract: To clarify the predictive factors of significant platelet count improvement in thrombocytopenic chronic hepatitis C (CHC) patients. CHC patients with baseline platelet counts of 〈 150 × 103/μL receiving direct-acting antiviral (DAA) therapy with at least 12-weeks post-treatment follow-up (PTW12) were enrolled. Significant platelet count improvement was defined as a ≥10% increase in platelet counts at PTW12 from baseline. Platelet count evolution at treatment week 4, end-of-treatment, PTW12, and PTW48 was evaluated. This study included 4922 patients. Sustained virologic response after 12 weeks post-treatment was achieved in 98.7% of patients. Platelet counts from baseline, treatment week 4, and end-of-treatment to PTW12 were 108.8 ± 30.2, 121.9 ± 41.1, 123.1 ± 43.0, and 121.1 ± 40.8 × 103/μL, respectively. Overall, 2230 patients (45.3%) showed significant platelet count improvement. Multivariable analysis revealed that age (odds ratio (OR) = 0.99, 95% confidence interval (CI): 0.99–1.00, p = 0.01), diabetes mellitus (DM) (OR = 1.20, 95% CI: 1.06–1.38, p = 0.007), cirrhosis (OR = 0.66, 95% CI: 0.58–0.75, p 〈 0.0001), baseline platelet counts (OR = 0.99, 95% CI: 0.98–0.99, p 〈 0.0001), and baseline total bilirubin level (OR = 0.80, 95% CI: 0.71–0.91, p = 0.0003) were independent predictive factors of significant platelet count improvement. Subgroup analyses showed that patients with significant platelet count improvement and sustained virologic responses, regardless of advanced fibrosis, had a significant increase in platelet counts from baseline to treatment week 4, end-of-treatment, PTW12, and PTW48. Young age, presence of DM, absence of cirrhosis, reduced baseline platelet counts, and reduced baseline total bilirubin levels were associated with significant platelet count improvement after DAA therapy in thrombocytopenic CHC patients.
    Type of Medium: Online Resource
    ISSN: 1999-4915
    URL: Article
    DOI: 10.3390/v14020333
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2516098-9
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  • 10
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    Clinical Efficacy of Adjuvant Chemotherapy in Advanced Upper Tract Urothelial Carcinoma (pT3-T4): Real-World Data from the Taiwan Upper Tract Urothelial Carcinoma Collaboration Group
    MDPI AG ; 2022
    In:  Journal of Personalized Medicine Vol. 12, No. 2 ( 2022-02-06), p. 226-
    Details
    In: Journal of Personalized Medicine, MDPI AG, Vol. 12, No. 2 ( 2022-02-06), p. 226-
    Abstract: The clinical efficacy of adjuvant chemotherapy in upper tract urothelial carcinoma (UTUC) is unclear. We aimed to assess the therapeutic outcomes of adjuvant chemotherapy in patients with advanced UTUC (pT3-T4) after radical nephroureterectomy (RNU). We retrospectively reviewed the data of 2108 patients from the Taiwan UTUC Collaboration Group between 1988 and 2018. Comprehensive clinical features, pathological characteristics, and survival outcomes were recorded. Univariate and multivariate Cox proportional hazards models were used to evaluate overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). Of the 533 patients with advanced UTUC included, 161 (30.2%) received adjuvant chemotherapy. In the multivariate analysis, adjuvant chemotherapy was significantly associated with a reduced risk of overall death (hazard ratio (HR), 0.599; 95% confidence interval (CI), 0.419–0.857; p = 0.005), cancer-specific mortality (HR, 0.598; 95% CI, 0.391–0.914; p = 0.018), and cancer recurrence (HR, 0.456; 95% CI, 0.310–0.673; p 〈 0.001). The Kaplan–Meier survival analysis revealed that patients receiving adjuvant chemotherapy had significantly better five-year OS (64% vs. 50%, p = 0.002), CSS (70% vs. 62%, p = 0.043), and DFS (60% vs. 48%, p = 0.002) rates compared to those who did not receive adjuvant chemotherapy. In conclusion, adjuvant chemotherapy after RNU had significant therapeutic benefits on OS, CSS, and DFS in advanced UTUC.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    URL: Article
    DOI: 10.3390/jpm12020226
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662248-8
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