METHODS: The imaging performance of the camera system was assessed quantitatively and qualitatively at different source depths, source to collimator distances (SCD), activity levels, acquisition times and source separations, utilising bespoke phantoms.
RESULTS: The system sensitivity and spatial resolution of the HGC for 125I were 0.41 cps/MBq (at SCD 48 mm) and 1.53 ± 0.23 mm (at SCD 10 mm) respectively. The camera was able to detect the 125I seed at a SCD of 63 mm (with no scattering material in place) in images recorded within a 1-min acquisition time. The detection of the seeds beneath scattering material (simulating deep-seated tumours) was limited to depths of less than 20 mm beneath the skin surface with a SCD of 63 mm and seed activity of 2.43 MBq. Subjective assessments of the hybrid images acquired showed the capability of the HGC for localising the 125I seeds.
CONCLUSION: This preliminary ex vivo study demonstrates that the HGC is capable of detecting 125I seeds and could be a useful tool in radioactive seed localisation with the added benefit of providing hybrid optical γ images for guiding breast conserving surgery.
ADVANCES IN KNOWLEDGE: The SFOV HGC could provide high resolution fused optical-gamma images of 125I radioactive seeds indicating the potential use in intraoperative surgical procedure such as RSL.
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.
METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.
RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.
CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.
METHODS: The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network.
RESULTS: The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively.
CONCLUSION: A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.