A deep radiomics model can be used to diagnose osteoporosis on hip radiographs with superior diagnostic performance than models using only clinical and texture features, which may enable opportunis... Purpose To develop and validate deep radiomics models for the diagnosis of osteoporosis using hip radiographs. Materials and Methods A deep radiomics model was developed using 4924 hip radiographs ...

This article reports the first application of a convolutional neural network (CNN) to in vivo X-ray fluorescence (XRF) images of gold nanoparticles (GNPs) obtained by a benchtop X-ray system to eliminate Compton-scattered photons. The XRF imaging system comprises a 2D CZT gamma camera, a pinhole collimator, and fan-beam polychromatic X-rays. An architecture of the 2D CNN model for Compton background elimination was optimally designed and trained with datasets obtained by the measurements of water only and GNP-embedded imaging phantoms. The XRF images generated by the trained 2D CNN were compared with those generated by the direct subtraction method. The developed 2D CNN was also applied to generate in vivo XRF images of gold in living mice exposed to GNPs. The in vivo XRF images generated by the two methods were then compared in terms of the difference in GNP concentrations. The results demonstrate that the 2D CNN model was successfully applied to in vivo XRF images for detecting the concentration and location of GNPs in living mice. This also implies that in vivo XRF images can be acquired using the 2D CNN without pre-injection scanning; thus, the image acquisition time and imaging dose can be reduced by half. Further, in vivo XRF images of GNPs can be acquired at any time after the injection, as the 2D CNN eliminates the need to restrict a living object during the inter-scans.

Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.

Echocardiography is the gold standard of diagnosis and evaluation of patent ductus arteriosus (PDA), a common condition among preterm infants that can cause hemodynamic abnormalities and increased mortality rates, but this technique requires a skilled specialist and is not always available. Meanwhile, chest X-ray (CXR) imaging is also known to exhibit signs of PDA and is a routine imaging modality in neonatal intensive care units. In this study, we aim to find and objectively define CXR image features that are associated with PDA by training and visually analyzing a deep learning model. We first collected 4617 echocardiograms from neonatal intensive care unit patients and 17,448 CXR images that were taken 4 days before to 3 days after the echocardiograms were obtained. We trained a deep learning model to predict the presence of severe PDA using the CXR images, and then visualized the model using GradCAM++ to identify the regions of the CXR images important for the model’s prediction. The visualization results showed that the model focused on the regions around the upper thorax, lower left heart, and lower right lung. Based on these results, we hypothesized and evaluated three radiographic features of PDA: cardiothoracic ratio, upper heart width to maximum heart width ratio, and upper heart width to thorax width ratio. We then trained an XGBoost model to predict the presence of severe PDA using these radiographic features combined with clinical features. The model achieved an AUC of 0.74, with a high specificity of 0.94. Our study suggests that the proposed radiographic features of CXR images can be used as an auxiliary tool to predict the presence of PDA in preterm infants. This can be useful for the early detection of PDA in neonatal intensive care units in cases where echocardiography is not available.