METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.
RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.
CONCLUSION: The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.
METHODS: This was a descriptive, retrospective analysis, conducted at the Department of Paediatrics, University Malaya Medical Centre, Malaysia. All children who had esophagogastroduodenoscopy (EGD) and colonoscopy from January 2008 to June 2011 were included. An endoscopy was considered appropriate when its indication complied with the NASPGHAN and ASGE guideline. All endoscopic findings were classified as either positive (presence of any endoscopic or histologic abnormality) or negative (no or minor abnormality, normal histology); effecting a positive contributive (a change in therapeutic decisions or prognostic consequences) or non-contributive yield (no therapeutic or prognostic consequences).
RESULTS: Overall, 76% of the 345 procedures (231 EGD alone, 26 colonoscopy alone, 44 combined EGD and colonoscopy) performed in 301 children (median age 7.0 years, range 3 months to 18 years) had a positive endoscopic finding. Based on the NASPGHAN and ASGE guideline, 99.7% of the procedures performed were considered as appropriate. The only inappropriate procedure (0.3%) was in a child who had EGD for assessment of the healing of gastric ulcer following therapy in the absence of any symptoms. The overall positive contributive yield for a change in diagnosis and/or management was 44%. The presence of a positive endoscopic finding was more likely to effect a change in the therapeutic plan than an alteration of the initial diagnosis. A total of 20 (5.8%) adverse events were noted, most were minor and none was fatal.
CONCLUSION: The NASPGHAN and ASGE guideline is more likely to predict a positive endoscopic finding but is less sensitive to effect a change in the initial clinical diagnosis or the subsequent therapeutic plan.
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
METHODS: SARS-CoV-2 antigens were immobilized on nitrocellulose membrane to capture human IgG, which was then detected with anti-human IgG conjugated gold nanoparticle (hIgG-AuNP). A total of 181 samples were analyzed in-house. Within which 35 were further evaluated in US FDA-approved CLIA Elecsys SARS-CoV-2 assay. The positive panel consisted of RT-qPCR positive samples from patients with both <14 days and >14 days from the onset of clinical symptoms. The negative panel contained samples collected from the pre-pandemic era dengue patients and healthy donors during the pandemic. Moreover, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FT-DBA were evaluated against RT-qPCR positive sera. However, the overall efficacies were assessed with sera that seroconverted against either nucleocapsid (NCP) or receptor-binding domain (RBD).
RESULTS: In-house ELISA selected a total of 81 true seropositive and 100 seronegative samples. The sensitivity of samples with <14 days using FT-DBA was 94.7%, increasing to 100% for samples >14 days. The overall detection sensitivity and specificity were 98.8% and 98%, respectively, whereas the overall PPV and NPV were 99.6% and 99%. Moreover, comparative analysis between in-house ELISA assays and FT-DBA revealed clinical agreement of Cohen's Kappa value of 0.944. The FT-DBA showed sensitivity and specificity of 100% when compared with commercial CLIA kits.
CONCLUSION: The assay can confirm past SARS-CoV-2 infection with high accuracy within 2 minutes compared to commercial CLIA or in-house ELISA. It can help track SARS-CoV-2 disease progression, population screening, and vaccination response. The ease of use of the assay without requiring any instruments while being semi-quantitative provides the avenue of its implementation in remote areas around the globe, where conventional serodiagnosis is not feasible.
METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.
RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.
DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.
METHODS: Stiffness index (SI) was measured and T-scores generated against an Asian database were recorded for 598,757 women and 173,326 men aged over 21 years old using Lunar Achilles (GE Healthcare) heel scanners. The scanners were made available in public centres in Singapore, Vietnam, Malaysia, Taiwan, Thailand, Indonesia and the Philippines.
RESULTS: The mean SI was higher for men than women. In women SI as well as T-scores declined slowly until approximately 45 years of age, then declined rapidly to reach a mean T-score of 80 years.
CONCLUSIONS: The heel scan data shows a high degree of poor bone health in both men and women in Asian countries, raising concern about the possible increase in fractures with ageing and the expected burden on the public health system.