Rapid Targeted Sequencing Using Dried Blood Spot Samples for Patients With Suspected Actionable Genetic Diseases.

Background: New genome sequencing technologies with enhanced diagnostic efficiency have emerged. Rapid and timely diagnosis of treatable rare genetic diseases can alter their medical management and clinical course. However, multiple factors, including ethical issues, must be considered. We designed a targeted sequencing platform to avoid ethical issues and reduce the turnaround time. Methods: We designed an automated sequencing platform using dried blood spot samples and a NEOseq_ACTION panel comprising 254 genes associated with Mendelian diseases having curable or manageable treatment options. Retrospective validation was performed using data from 24 genetically and biochemically confirmed patients. Prospective validation was performed using data from 111 patients with suspected actionable genetic diseases. Results: In prospective clinical validation, 13.5% patients presented with medically actionable diseases, including short- or medium-chain acyl-CoA dehydrogenase deficiencies (N=6), hyperphenylalaninemia (N=2), mucopolysaccharidosis type IVA (N=1), alpha thalassemia (N=1), 3-methylcrotonyl-CoA carboxylase 2 deficiency (N=1), propionic acidemia (N=1), glycogen storage disease, type IX(a) (N=1), congenital myasthenic syndrome (N=1), and citrullinemia, type II (N=1). Using the automated analytic pipeline, the turnaround time from blood collection to result reporting was <4 days. Conclusions: This pilot study evaluated the possibility of rapid and timely diagnosis of treatable rare genetic diseases using a panel designed by a multidisciplinary team. The automated analytic pipeline maximized the clinical utility of rapid targeted sequencing for medically actionable genes, providing a strategy for appropriate and timely treatment of rare genetic diseases.

Deep learning segmentation of major vessels in X-ray coronary angiography.

X-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.