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Because osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate.For model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with20% or ≥20-25% vertebral height loss respectively, moderate grade with ≥25-40% vertebral height loss, severe grade with ≥40%-2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed 'base-model 1.0'. A user-friendly interface was also developed, with the synthesized software termed 'Ofeye 1.0'.Counting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows 'batch processing', for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer.A user-friendly software program was developed for CVF detection on elderly women's lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women.

Aim: Liver vessel density can be evaluated by DDVD (diffusion derived vessel density): DDVD(b0b1) = Sb0/ROIarea0-Sb1/ROIarea1, where Sb0 and Sb1 refer to the liver signal when b is 0 or 1 (s/mm 2); ROIarea0 and ROIarea1 refer to the region-of-interest on b= 0 or 1 images. Sb1 and ROIarea1 may be replaced by Sb25 and ROIarea25 or Sb50 and ROIarea50 (i.e., by images of b=25 or 50 s/mm 2). With rat biliary duct ligation (BDL) model, this study assessed the usefulness of liver DDVD computed from a simplified IVIM imaging protocol. Materials and Methods: Male Sprague-Dawley rats were used, and 1, 2, 3, 4 weeks post BDL had rat number of 5, 5, 5,3 respectively. Partial recanalization (n=12) was performed with rats had BDL for 7 days and MRI was done mean 14 days after recanalization. Liver diffusion MR images were acquired using with a 3.0T magnet with a b-value distribution of 0, 30, 50, 75, 100, 150, 300, 700, 1000 s/mm 2. DDVDmean (control rats n=6) was the mean of DDVD(b0b25) and DDVD(b0b50). IVIM fitting started from b = 0 with segmented fitting and a threshold b of 50 s/mm 2 (n=5 for control rats). Three 3-D space was constructed using PF, D slow , and D fast ; PF, D slow , and DDVD mean ; or PF, D fast , and DDVD mean. Results: BDL week 1, 2, 3, 4 rats and partial recanalization rats had mean liver fibrosis METAVIR score of 1.5, 2.4, 3.25, 3.7 and 2.42 respectively. The control rats and BDL rats (n=18) had a liver DDVD mean of 84.0±26.2 and 44.7±14.4 au/pixel (p

This study estimated the bone mineral density (BMD) defined osteoporosis prevalence of Chinese women and Chinese men aged ≥50 years. The estimation was based on the 1994 WHO definition of osteoporosis and two assumptions: (I) fragility fracture (FF) risk among older Chinese is half of that of older US Caucasians; (II) FF risk among older Chinese men is half of that of older Chinese women. In addition, we also consider the FF risk among older Chinese is close to those of American Blacks. We estimated that the osteoporosis prevalence based on lumbar BMD, femoral neck BMD, total hip BMD would be 15.8%, 20.4%, and 15.2% for US Caucasian women, 6.7%, 7.8%, and 7.9% for US black women, 7.5%, 7.5%, and 6.7% for Chinese women, 1.8%, 5.7%, and 3.3% for US black men, and 2.0%, 3.8%, and 3.4% for Chinese men. To satisfy the above estimates of osteoporosis prevalence for the Chinese population, in addition to using a local reference database, we suggest that the T-score cutpoints for defining osteopenia and osteoporosis among older Chinese should be adjusted from the conventional WHO thresholds of -2.5 and -1.0. Our suggested revised cutpoint T-score for defining osteoporosis described in this article will be more in line with the original WHO definition and will allow a more meaningful international comparison of disease burden.

Background Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI. Methods Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups. Results The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control. Conclusions The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.

Background: Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer’s disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. Methods: In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans. Results: The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction. Conclusions: The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI.