Automated chest X-ray reading for tuberculosis in the Philippines to improve case detection: a cohort study.

BACKGROUND: DetecTB (Diagnostic Enhanced Tools for Extra Cases of TB), an intensified tuberculosis (TB) case-finding programme targeting prisons and high-risk communities was implemented on Palawan Island, the Philippines.OBJECTIVE: To evaluate the performance of TB detection based on computerised chest radiography (CXR) readings.DESIGN: Data from 14 094 subjects were analysed from September 2012 to June 2014. All CXRs were read by a physician and by software. Individuals with TB symptoms or CXR abnormalities according to the physician underwent Xpert® MTB/RIF testing, the remaining persons were considered TB-negative (screening reference). A subset of 200 CXRs was read by an independent human reader (radiological reference). This reader also re-read a subset of the most abnormal cases as identified using the software but read as normal by the physician (discordant cases).RESULTS: A total of 10 755 individuals were included in the analysis, 2534 of whom had a positively assessed CXR; 298 cases were Xpert-positive. Using the screening reference, the area under the receiver operating characteristic curve for software readings was 0.93 (95%CI 0.92-0.94), with a sensitivity of 0.98 (95%CI 0.97-0.99) and a specificity of 0.69 (95%CI 0.40-0.98). Based on the radiological reference, the physician performed slightly worse than the software (sensitivity, 0.82, 95%CI 0.74-0.89 and specificity, 0.87, 95%CI 0.81-0.96 vs. sensitivity, 0.83, 95%CI 0.71-0.93 and specificity, 0.87, 95%CI 0.75-0.95), although this was not statistically significant. Of the 291 discordant cases, 70% were assessed as positive, resulting in a 22% increase in TB detection when extrapolated to the full cohort.CONCLUSION: The performance of automated CXR reading is comparable to that of the attending physicians in DetecTB, and its use as a second reader could increase TB detection.

Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening.

SETTING: Tuberculosis (TB) screening programmes can be optimised by reducing the number of chest radiographs (CXRs) requiring interpretation by human experts. OBJECTIVE: To evaluate the performance of computerised detection software in triaging CXRs in a high-throughput digital mobile TB screening programme. DESIGN: A retrospective evaluation of the software was performed on a database of 38 961 postero-anterior CXRs from unique individuals seen between 2005 and 2010, 87 of whom were diagnosed with TB. The software generated a TB likelihood score for each CXR. This score was compared with a reference standard for notified active pulmonary TB using receiver operating characteristic (ROC) curve and localisation ROC (LROC) curve analyses. RESULTS: On ROC curve analysis, software specificity was 55.71% (95%CI 55.21-56.20) and negative predictive value was 99.98% (95%CI 99.95-99.99), at a sensitivity of 95%. The area under the ROC curve was 0.90 (95%CI 0.86-0.93). Results of the LROC curve analysis were similar. CONCLUSION: The software could identify more than half of the normal images in a TB screening setting while maintaining high sensitivity, and may therefore be used for triage.

Short-Term Outcomes of 100 Consecutive Kidney Transplantations in a 3-Year Period: A Single-Center Experience.

BACKGROUND: The results of kidney transplantation have improved significantly in the last decade with patient and graft survival rates that range from 92% to 95%. METHODS: We analyzed the clinical results in the last 100 consecutive patients with a follow-up of 6-42 months at our institution. We also made a general evaluation of the patients before surgery as candidates for transplantation and divided them into 3 groups (good, moderate, and poor). RESULTS: We had 8 living donors and 92 cadaveric kidney transplantation cases. Principal cause of donor death was cerebrovascular disease accounting for 64%. Mean age of recipients was 55.1 ± 12.9 years with a total of 65 males. Currently there are 96 functioning allografts. During this 3-year period, 2 patients suffered graft loss and 2 patients died with a functioning allograft. We studied whether there were statistically significant differences in renal function (Modification of Diet in Renal Disease Study Equation [MDRD]) at 12 months and at last visit with respect to the evaluation of recipient as candidate for renal transplantation. CONCLUSION: Our observations suggest great improvement of early results of renal transplantation in recent years, including complex cases. In this 3-year period we had a patient survival rate of 98% and a graft survival rate of 96% of cases. Further dedicated prospective studies that aim to evaluate or to propose possible recipient-related predictors for kidney transplantation outcomes in different populations are needed.

Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs.

Molecular tests hold great potential for tuberculosis (TB) diagnosis, but are costly, time consuming, and HIV-infected patients are often sputum scarce. Therefore, alternative approaches are needed. We evaluated automated digital chest radiography (ACR) as a rapid and cheap pre-screen test prior to Xpert MTB/RIF (Xpert). 388 suspected TB subjects underwent chest radiography, Xpert and sputum culture testing. Radiographs were analysed by computer software (CAD4TB) and specialist readers, and abnormality scores were allocated. A triage algorithm was simulated in which subjects with a score above a threshold underwent Xpert. We computed sensitivity, specificity, cost per screened subject (CSS), cost per notified TB case (CNTBC) and throughput for different diagnostic thresholds. 18.3% of subjects had culture positive TB. For Xpert alone, sensitivity was 78.9%, specificity 98.1%, CSS $13.09 and CNTBC $90.70. In a pre-screening setting where 40% of subjects would undergo Xpert, CSS decreased to $6.72 and CNTBC to $54.34, with eight TB cases missed and throughput increased from 45 to 113 patients/day. Specialists, on average, read 57% of radiographs as abnormal, reducing CSS ($8.95) and CNTBC ($64.84). ACR pre-screening could substantially reduce costs, and increase daily throughput with few TB cases missed. These data inform public health policy in resource-constrained settings.

Localized Energy-Based Normalization of Medical Images: Application to Chest Radiography.

Automated quantitative analysis systems for medical images often lack the capability to successfully process images from multiple sources. Normalization of such images prior to further analysis is a possible solution to this limitation. This work presents a general method to normalize medical images and thoroughly investigates its effectiveness for chest radiography (CXR). The method starts with an energy decomposition of the image in different bands. Next, each band's localized energy is scaled to a reference value and the image is reconstructed. We investigate iterative and local application of this technique. The normalization is applied iteratively to the lung fields on six datasets from different sources, each comprising 50 normal CXRs and 50 abnormal CXRs. The method is evaluated in three supervised computer-aided detection tasks related to CXR analysis and compared to two reference normalization methods. In the first task, automatic lung segmentation, the average Jaccard overlap significantly increased from 0.72±0.30 and 0.87±0.11 for both reference methods to with normalization. The second experiment was aimed at segmentation of the clavicles. The reference methods had an average Jaccard index of 0.57±0.26 and 0.53±0.26; with normalization this significantly increased to . The third experiment was detection of tuberculosis related abnormalities in the lung fields. The average area under the Receiver Operating Curve increased significantly from 0.72±0.14 and 0.79±0.06 using the reference methods to with normalization. We conclude that the normalization can be successfully applied in chest radiography and makes supervised systems more generally applicable to data from different sources.

Active learning for an efficient training strategy of computer-aided diagnosis systems: application to diabetic retinopathy screening.

The performance of computer-aided diagnosis (CAD) systems can be highly influenced by the training strategy. CAD systems are traditionally trained using available labeled data, extracted from a specific data distribution or from public databases. Due to the wide variability of medical data, these databases might not be representative enough when the CAD system is applied to data extracted from a different clinical setting, diminishing the performance or requiring more labeled samples in order to get better data generalization. In this work, we propose the incorporation of an active learning approach in the training phase of CAD systems for reducing the number of required training samples while maximizing the system performance. The benefit of this approach has been evaluated using a specific CAD system for Diabetic Retinopathy screening. The results show that (1) using a training set obtained from a different data source results in a considerable reduction of the CAD performance; and (2) using active learning the selected training set can be reduced from 1000 to 200 samples while maintaining an area under the Receiver Operating Characteristic curve of 0.856.

Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy.

An automatic method to detect hard exudates, a lesion associated with diabetic retinopathy, is proposed. The algorithm found on their color, using a statistical classification, and their sharp edges, applying an edge detector, to localize them. A sensitivity of 79.62% with a mean number of 3 false positives per image is obtained in a database of 20 retinal image with variable color, brightness and quality. In that way, we evaluate the robustness of the method in order to make adequate to a clinical environment. Further efforts will be done to improve its performance.

Only one fitting for bironchigrams.

We present an algorithm that uses a square grid in a Ronchi test. We assume that the point coordinates of this pattern (termed a bironchigram) are affected by Gaussian errors. To calculate the optical path difference, we apply only one nonlinear least-squares fit to the dot coordinates. The relevant equations are deduced, and experimental results are shown.

The proinflammatory cytokine, interleukin-1alpha, reduces glucocorticoid receptor translocation and function.

Proinflammatory cytokines have been shown to influence the expression and function of the glucocorticoid receptor (GR). Specifically, several studies have found that cytokines induce a decrease in GR function, as evidenced by reduced sensitivity to glucocorticoid effects on functional end points. To investigate the potential mechanism(s) involved, we examined the impact of the proinflammatory cytokine, interleukin-1alpha (IL-1alpha), on 1) GR translocation from cytoplasm to nucleus using GR immunostaining, 2) cytosolic radioligand GR binding, and 3) GR-mediated gene transcription in L929 cells stably transfected with the mouse mammary tumor virus-cholamphenicol acetyltransferase reporter gene. L929 cells were treated with IL-1alpha (100 and 1000 U/ml) for 24 h in the presence or absence of dexamethasone (Dex; 10 nM to 1 microM). IL-1alpha inhibited Dex-induced GR translocation and alone induced GR up-regulation. Pretreatment with IL-1alpha followed by Dex treatment for 1.5 h led to about 20% inhibition of Dex-induced GR-mediated gene transcription, whereas coincubation of IL-1alpha plus Dex for 24 h inhibited Dex-induced GR-mediated gene activity up to 42%. The latter effect was reversed by the IL-1 receptor antagonist. These results suggest that cytokines produced during an inflammatory response may induce GR resistance in relevant cell types by direct effects on the GR, thereby providing an additional pathway by which the immune system can influence the hypothalamic-pituitary-adrenal axis.