Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging.

BACKGROUND: Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses. OBJECTIVE: This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging. METHODS AND MATERIALS: This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy. RESULTS: Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors. CONCLUSIONS: This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.

Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks.

BACKGROUND: Dividing liver organs or lesions depicting on computed tomography (CT) images could be applied to help tumor staging and treatment. However, most existing image segmentation technologies use manual or semi-automatic analysis, making the analysis process costly and time-consuming. OBJECTIVE: This research aims to develop and apply a deep learning network architecture to segment liver tumors automatically after fine tuning parameters. METHODS AND MATERIALS: The medical imaging is obtained from the International Symposium on Biomedical Imaging (ISBI), which includes 3D abdominal CT scans of 131 patients diagnosed with liver tumors. From these CT scans, there are 7,190 2D CT images along with the labeled binary images. The labeled binary images are regarded as gold standard for evaluation of the segmented results by FCN (Fully Convolutional Network). The backbones of FCN are extracted from Xception, InceptionresNetv2, MobileNetv2, ResNet18, ResNet50 in this study. Meanwhile, the parameters including optimizers (SGDM and ADAM), size of epoch, and size of batch are investigated. CT images are randomly divided into training and testing sets using a ratio of 9:1. Several evaluation indices including Global Accuracy, Mean Accuracy, Mean IoU (Intersection over Union), Weighted IoU and Mean BF Score are applied to evaluate tumor segmentation results in the testing images. RESULTS: The Global Accuracy, Mean Accuracy, Mean IoU, Weighted IoU, and Mean BF Scores are 0.999, 0.969, 0.954, 0.998, 0.962 using ResNet50 in FCN with optimizer SGDM, batch size 12, and epoch 9. It is important to fine tuning the parameters in FCN model. Top 20 FNC models enable to achieve higher tumor segmentation accuracy with Mean IoU over 0.900. The occurred frequency of InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception are 9, 6, 3, 5, and 2 times. Therefore, the InceptionresNetv2 has higher performance than others. CONCLUSIONS: This study develop and test an automated liver tumor segmentation model based on FCN. Study results demonstrate that many deep learning models including InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception have high potential to segment liver tumors from CT images with accuracy exceeding 90%. However, it is still difficult to accurately segment tiny and small size tumors by FCN models.

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.

Postural control decreases with aging. Thus, an efficient and accurate method of detecting postural control is needed. We enrolled 35 elderly adults (aged 82.06 ± 8.74 years) and 20 healthy young adults (aged 21.60 ± 0.60 years) who performed standing tasks for 40 s, performed six times. The coordinates of 15 joint nodes were captured using a Kinect device (30 Hz). We plotted joint positions into a single 2D figure (named a joint-node plot, JNP) once per second for up to 40 s. A total of 15 methods combining deep and machine learning for postural control classification were investigated. The accuracy, sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and kappa values of the selected methods were assessed. The highest PPV, NPV, accuracy, sensitivity, specificity, and kappa values were higher than 0.9 in validation testing. The presented method using JNPs demonstrated strong performance in detecting the postural control ability of young and elderly adults.

Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images.

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.

Classification of the Multiple Stages of Parkinson's Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images.

Single photon emission computed tomography (SPECT) has been employed to detect Parkinson's disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models' performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

Questionable necessity of nitroglycerin for diagnostic coronary artery examination using 320-row multi-detector computed tomography.

OBJECTIVE: This study aims to analyze and compare the diagnostic effectiveness of 320-row multi-detector computed tomography for coronary artery angiography (MDCTA) in subjects with and without sublingual vasodilator (nitroglycerin). MATERIALS AND METHODS: From September 2015 to September 2016, 70 individuals without history of major cardiovascular diseases who underwent MDCTA for health examination were retrospectively categorized into sublingual nitroglycerin (NTG) and non-NTG groups. Medical history, CT dose index (CTDI), and multi-slice CT images were compared between two groups. A diameter of coronary artery (DA, mm) was computed and analyzed. RESULTS: A total of 41 males and 29 females (mean age: 55.43±8.84 years, range: 34- 76) were reviewed. Normal and abnormal MDCTA findings were noted in 54 and 16 participants, respectively, with the detection rate of coronary artery disease being 23%. There was no significant difference in inter-observer variability of coronary CTA image quality and diagnosis between the NTG and non-NTG groups among three experienced radiologists. Although the percentage dilatation of left anterior descending branch (LAD), right coronary artery (RCA) and left circumflex branch (LCX) following in the NTG group were 12.4%, 12.8% and 25.3%, respectively (p < 0.01), there was no significant difference in image quality and diagnosis between the two groups. CONCLUSIONS: Despite the recommendation of routine nitroglycerin use for subjects undergoing computed tomography for coronary artery angiography, our results showed no significant advantage of its use in improving image quality and rate of diagnosis accuracy.

Integrate Weather Radar and Monitoring Devices for Urban Flooding Surveillance.

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

Feasible Classified Models for Parkinson Disease from 99mTc-TRODAT-1 SPECT Imaging.

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson's disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky's skewness coefficient, Pearson's median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

The effect of music therapy on reducing depression in people with dementia: A systematic review and meta-analysis.

The aim of this study was to determine the effectiveness of music therapy on reducing depression for people with dementia during different intervention intervals. A systematic review with a meta-analysis of randomized controlled trials. The databases surveyed include AgeLine, CINAHL, MEDLINE, PsycINFO, PubMed, and Cochrane. Seven studies were included in this review. The result revealed that music therapy significantly reduced depression at six, eight, and 16 weeks. This study also supported that music therapy significantly improved depression when the results of six studies with medium-term interventions were pooled. However, no evidence of effect of music therapy on depression was observed at three, four, 12 weeks, and five months during intervention, and one and two months after the cease of music therapy. Music therapy without a music therapist involved did not significantly reduce depression at any time. Medium-term of music therapy might be appropriate in reducing depression for people with dementia.

Effects of nurse-led lower extremity strength training on knee function recovery in patients who underwent total knee replacement.

AIMS AND OBJECTIVES: To examine the effects of lower extremity muscle strength training on knee function recovery and quality of life in patients who underwent total knee replacement. BACKGROUND: Patients with knee osteoarthritis after surgery experience decreased knee function that impacts their quality of life. However, patients typically lack a long-term, home-based and continuous leg exercise training method and rarely have studies explored the effects of exercise training on knee function recovery and quality of life. DESIGN: A experimental and longitudinal study design. METHODS: The simple randomised sampling (based on patients' admission priority order) was used to collect participant data. Outcome measurements included the Knee Injury and Osteoarthritis Outcome Score. Participants were randomised to receive and starting lower extremity muscle strength training before surgery (training group, n = 100) or to receive usual care (nontraining group, n = 100). Data were collected and followed up with the patients before surgery (T1) and at 2 weeks (T2), 1 month (T3), 2 months (T4) and 3 months (T5) after discharge. RESULTS: The Knee Injury and Osteoarthritis Outcome Score subscale scores showed that both groups of patients experienced knee function and quality of life decreases 2 weeks after total knee replacement, but all subscale scores gradually increased from the first month to the third month after total knee replacement. Both groups and times were significantly different, but the training group's knee function and quality of life recovered earlier and better than the nontraining group does. CONCLUSIONS: This study confirmed that lower extremity muscle strength training helps to improve quality of life and knee function in patients who undergo total knee replacement. Healthcare staff should include this training in presurgical nursing care and in patients' discharge plans as a continuous, daily rehabilitation activity at home. RELEVANCE TO CLINICAL PRACTICE: When patients are diagnosed with knee osteoarthritis and undergo surgery, a presurgical exercise education and discussion of knee function rehabilitation should be part of standard care.

The risk of cataractogenesis after gamma knife radiosurgery: a nationwide population based case-control study.

BACKGROUND: Medical radiation is considered a factor responsible for cataractogenesis. However, the incidence of this ophthalmologic complication resulting from gamma knife radiosurgery (GKRS) has not yet been reported. The present study aimed to determine the risk of cataractogenesis associated with radiation exposure from GKRS. METHODS: This study used information from a random sample of one million persons enrolled in the nationally representative Taiwan National Health Insurance Research Database. The GK group consisted of patients who underwent GKRS between 2000 and 2009. The non-GK group was composed of subjects who had never undergone GKRS, but who were matched with the case group for time of enrollment, age, sex, history of coronary artery disease, hypertension, and diabetes. RESULTS: There were 277 patients in the GK group and 2770 matched subjects in the non-GK group. The GK group had a higher overall incidence of cataracts (10.11% vs. 7.26%; crude hazard ratio [cHR], 1.59; 95% CI, 1.07-2.36; adjusted hazard ratio [aHR], 1.25; 95% CI, 0.82-1.90) than the non-GK group. Patients who had undergone computed tomography and/or cerebral angiography (CT/angio) studies had a higher risk of developing cataracts than those who did not (10.82% vs. 6.64%; cHR, 1.74; 95% CI, 1.31-2.30; aHR, 1.65; 95% CI, 1.22-2.23). The age group between 30 and 50 years had the highest risk of cataractogenesis in both the GK and CT/angio groups (cHR, 3.50; 95% CI, 1.58-7.72; aHR, 2.43; 95% CI, 1.02-5.81; cHR, 2.96; 95% CI, 1.47-5.99; aHR, 2.27; 95% CI, 1.05-4.93, respectively). CONCLUSIONS: Radiation exposure due to GKRS and CT/angio study may be independently associated with increased risk of cataractogenesis. We suggest routine dosimetry measurement of eye lens and proper protection for patients with benign lesions during GKRS. Regular follow-up imaging studies should avoid the use of CT/angio, and particular care should be taken in the 30-50-year-old age group, due to their significantly increased risk of cataract formation.

Diagnosed chest lesion on diffusion-weighted magnetic resonance images using apparent diffusion coefficients.

PURPOSE: A novel diagnostic method using the standard deviation (SD) value of apparent diffusion coefficient (ADC) by diffusion-weighted (DWI) magnetic resonance imaging (MRI) is applied for differential diagnosis of primary chest cancers, metastatic tumors and benign tumors. MATERIALS AND METHODS: This retrospective study enrolled 27 patients (20 males, 7 female; age, 15-85; mean age, 68) who had thoracic mass lesions in the last three years and underwent an MRI chest examination at our institution. In total, 29 mass lesions were analyzed using SD of ADC and DWI. Lesions were divided into five groups: Primary lung cancers (N = 10); esophageal cancers (N = 5); metastatic tumors (N = 8); benign tumors (N = 3); and inflammatory lesions (N = 3). Quantitative assessment of MRI parameters of mass lesions was performed. The ADC value was acquired based on the average of the entire tumor area. The error-plot, t-test and the area under receiver operating characteristic (AUC) were applied for statistical analysis. RESULTS: The SD of ADC value (mean±SD) was (4.867±1.359)×10-4 mm2/sec in primary lung cancers, and (3.598±0.350)×10-4 mm2/sec in metastatic tumors. The SD of ADC values of primary lung cancers and metastatic tumors (P <  0.05) were significantly different and the AUC was 0.800 (P <  0.05). The means of SD of ADC values was 4.532±1.406×10-4 mm2/sec and 2.973±0.364×10-4 mm2/sec for malignant tumors (including primary lung cancers, esophageal cancers) and benign tumors with respectively. The mean of SD of ADC values between malignant chest tumors and benign chest tumors was shown significant difference (P <  0.01). The values of AUC was 0.967 between malignant chest tumors and benign chest tumors (P <  0.05). The ADC values for primary lung cancers, metastatic tumors and benign tumors were not significantly difference (P >  0.05). CONCLUSIONS: The mean of SD of ADC value by DWI can be used for differential diagnosis of chest lesions.

Investigated geometrical characteristics and image density of left ventricle of multi-detector computed tomography in early coronary artery disease patients.

BACKGROUND: Coronary artery disease (CAD) remains the leading cause of death worldwide. Currently, cardiac multi-detector computed tomography (MDCT) is widely used to diagnose CAD. The purpose in this study is to identify informative and useful predictors from left ventricular (LV) in the early CAD patients using cardiac MDCT images. MATERIALS AND METHODS: Study groups comprised 42 subjects who underwent a screening health examination, including laboratory testing and cardiac angiography by 64-slice MDCT angiography. Two geometrical characteristics and one image density were defined as shape, size and stiffness on MDCT image. The t-test, logistic regression, and receiver operating characteristic curve were applied to assess and identify the significant predictors. The Kappa statistics was used to exam the agreements with physician's judgments (i.e., Golden of True, GOT). RESULTS: The proposed three characteristics of LV MDCT images are important predictors and risk factors for the early CAD patients. These predictors present over 80% of AUC and higher odds ratio. The Kappa statistics was 0.68 for the combinations of shape and stiffness into logistic regression. CONCLUSIONS: The shape, size and stiffness of the left ventricular on MDCT can be used to be the effective indicators in the early CAD patients. Besides, the combinations of shape and stiffness into logistic regression could provide substantial agreement with physician's judgments.

Lower urinary tract symptoms of prostate cancer patients undergoing treatments over eight-month follow-up.

AIMS AND OBJECTIVES: To examine the changes in lower urinary tract symptoms after open radical prostatectomy, laparoscopic radical prostatectomy and brachytherapy and to determine which treatment resulted in improved lower urinary tract symptoms at 8 months follow-up. BACKGROUND: Lower urinary tract symptoms are a primary side effect after prostate cancer treatment. DESIGN: A time-series survey design with descriptive and comparative elements. METHODS: A sample of 51 prostate cancer patients was recruited: open radical prostatectomy = 20, laparoscopic radical prostatectomy = 23 and brachytherapy = 8. Data were collected at six time points: before treatment/baseline, 1 week post-treatment, 1 month post-treatment, 2 month post-treatment, 3 month post-treatment and 8 months post-treatment. The lower urinary tract symptoms were assessed using the International Prostate Symptom Score, with a higher score indicating a worse condition. One-way anova was used to predict the progress of urinary symptoms after treatments. Bootstrap re-sampling was conducted to assess the stability of the outcomes. RESULTS: Although there were no significant differences in the lower urinary tract symptoms among the three groups after treatments, the laparoscopic radical prostatectomy group had the lowest International Prostate Symptom Score score at baseline. Compared with the baseline symptoms for patients undergoing each treatment, there were significant improvements after 2 months in the open radical prostatectomy and brachytherapy groups, and after 3 months in the laparoscopic radical prostatectomy group. CONCLUSIONS: The prostate cancer patients undergoing the three treatments have similar lower urinary tract symptoms over 8-month follow-up although different lower urinary tract symptoms were presented before treatments. RELEVANCE TO CLINICAL PRACTICE: Results could be applied to educating and counselling prostate cancer patients regarding symptoms during recovery after surgery. It could also help patients better understand the outcomes related to the differing treatment methods.

Scattering correction algorithm in the PET sinogram using the factorial design of experimental method: A phantom study.

Positron emission tomography (PET) had been utilized to image gene therapy, estimate tumor growth, detect neural function of the brain, and diagnose disease. However, sinogram noise always results inaccurate PET images. The factorial design of experiment (DOE), a statistical method, was applied to investigate, correct and estimate the fraction of scattering of 2D sinogram in PET. The DOE was included as factors of angle views and scatter media with two levels designed. The PET sinogram after scattering correction was then reconstructed by filtered back projection (FBP). Both Ge-68 uniform phantom and Jaszczak anthropomorphic torso phantom were applied to exam the performance of presented scattering correction algorithm. The signal-to-noise ratio (SNR), standard deviation (STD) of background, and full width at half maximum (FWHM), and uniformity test were applied to validate the performance of presented method. The proposed method provides a narrower FWHM, smaller STD of the background, higher SNR and better uniformity than those of original protocols. This method should be tested for accuracy and feasibility with three-dimensional phantoms or real animal studies and consideration effects of cross-talk between slices in future work.

Short-term effects of a suicide education intervention for family caregivers of people who are suicidal.

AIMS AND OBJECTIVES: To evaluate the short-term effects of a suicide care educational intervention on the family's ability to care, family's caring stress levels and family's attitudes towards attempted suicide. BACKGROUND: Research has demonstrated that suicide prevention educational programmes are provided mostly for professional staff and not for the family caregivers of people who are suicidal. DESIGN: A experimental design, using two groups and a pre- and postintervention survey method, was used. METHODS: A randomised controlled study was conducted with 74 family caregivers of people who are suicidal (37 using suicide education and 37 in the control group). The experimental group was provided with a two-hour suicide care education intervention, and the control group received normal suicide care support. Participants were recruited at a Suicide Prevention Centre and two acute psychiatric hospitals between October 2009-December 2010. Three questionnaires were collected: (1) the Suicidal Caring Ability Scale (2) the Caring Stress Scale and (3) the Suicide Attitudes Scale. Descriptive statistics, independent t-tests or Mann-Whitney U-tests were used to analyse the data. RESULTS: The results demonstrated that there were statistically significant differences in the Suicidal Caring Ability Scale and the Suicide Attitudes Scale but no statistically significant differences in the Caring Stress Scale. That is, the suicide education programme can promote the ability to care for people who are suicidal and can generate a positive attitude towards people who are suicidal from their caregivers. CONCLUSIONS: Family caregivers of suicidal individuals who attended the psycho-education programme had an increased caring ability and positive attitudes for their suicidal relatives. RELEVANCE TO CLINICAL PRACTICE: Nurses could use the two-hour personal suicidal education programme to increase one's ability to care for their relatives who had attempted suicide and promote one's positive attitudes towards attempted suicide.

Analyzed outcome for embolization of hepatic carcinoma by C-arm angiographic computed tomography.

PURPOSE: This study evaluated and monitored the outcome of angiographic embolization of hepatic carcinoma by real-time C-arm angiographic computed tomography under number of tumors, size of tumors, and patient's age.METHODS AND MARTIALS: In total, 142 patients underwent angiographic embolization of hepatic carcinoma. The control group, 71 patients, underwent conventional angiographic (CA) embolization of hepatic carcinoma. The experimental group, 71 patients, underwent C-arm angiographic computed tomography (CCT) embolization of hepatic carcinoma. The numbers of angiographic embolization, number of tumors, size of tumors, and patients ages were recorded for comparisons between groups by analysis of variance (ANOVA) with cross-interaction and the chi-square test (cross table). RESULTS: The age ranges were 20-84 and 35-84 years old for the experimental and control groups respectively. Average number of angiographic embolizations of hepatic carcinomas were 2.63 ± 1.84 and 5.32 ± 2.01 for the experimental and control groups. The number of angiographic embolizations under number of tumors, size of tumors, and patients ages between groups were significantly different (P< 0.05). The effective analyses of transcatheter arterial chemoembolization (TACE) by CCT were significant by chi-square test (P< 0.05) under ⩽ 3 cm and patients aged ⩽ 60. CONCLUSION: The main advantage by CCT for undergoing TACE under tumor size smaller than 3 cm and numbers of tumor smaller 3 times were more significantly effective than those by CA. The CCT combined with TACE had high potentially reduced numbers of undergoing TACE.

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.

Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.