Evaluierung von Normwerten zur Etablierung der Digitalen Radiogrammetrie in der klinischen Anwendung

Purpose: the study presents German reference data for Digital X-ray Radiogrammetry (DXR) differentiated by male as well as female, and quantifies gender-specific and agerelated differences for all DXR-parameters. The study also documents the effects of different X-ray settings (e.g. radiographs of the wrist or the hand) on DXR-measurements. Patients and Methods: 2085 patients were prospectively enrolled (954 female / 1131 male) from data pool of 11915 patients with radiographs of the non-dominant hand or wrist. All patients underwent measurements of bone mineral density (BMD), cortical thickness (CT), bone width (W) and Metacarpal Index (MCI) using DXR-technology. Results: These data show a continuous age-related increase of the DXR-parameters to the point of peak bone mass, then a continuous decline beyond the peak bone mass with accentuated age-related cortical bone loss in women. Peak bone mass is reached at about 30-35 years for woman and 45-50 for men. Additionally males show a significantly higher DXR-BMD (mean: +12,8%) compared to the female cohort for all age groups. Rgarding the impact of various X-ray settings (X-ray wrist versus X-ray hand) no significant difference was observed between both groups in men as well as women. Conclusion: in conclusion the development of digital imaging technology has enabled more precise measurement of several radiogeometric features. The present study has estimated normative reference values for Digital X-ray Radiogrammetry in German Caucasian woman and men. Based on this reference data a valid and reliable quantification of disease-related demineralisation based on measurements of DXR-BMD and MCI is now available for the Caucasian ethnic group

Clinical Study Reducing Radiation Dose in Emergency CT Scans While Maintaining Equal Image Quality: Just a Promise or Reality for Severely Injured Patients?

Objective. This study aims to assess the impact of adaptive statistical iterative reconstruction (ASIR) on CT imaging quality, diagnostic interpretability, and radiation dose reduction for a proven CT acquisition protocol for total body trauma. Methods. 18 patients with multiple trauma (ISS ≥ 16) were examined either with a routine protocol ( = 6), 30% ( = 6), or 40% ( = 6) of iterative reconstruction (IR) modification in the raw data domain of the routine protocol (140 kV, collimation: 40, noise index: 15). Study groups were matched by scan range and maximal abdominal diameter. Image noise was quantitatively measured. Image contrast, image noise, and overall interpretability were evaluated by two experienced and blinded readers. The amount of radiation dose reductions was evaluated. Results. No statistically significant differences between routine and IR protocols regarding image noise, contrast, and interpretability were present. Mean effective dose for the routine protocol was 25.3 ± 2.9 mSv, 19.7 ± 5.8 mSv for the IR 30, and 17.5 ± 4.2 mSv for the IR 40 protocol, that is, 22.1% effective dose reduction for IR 30 ( = 0.093) and 30.8% effective dose reduction for IR 40 ( = 0.0203). Conclusions. IR does not reduce study interpretability in total body trauma protocols while providing a significant reduction in effective radiation dose

Reducing Radiation Dose in Emergency CT Scans While Maintaining Equal Image Quality: Just a Promise or Reality for Severely Injured Patients?

Objective. This study aims to assess the impact of adaptive statistical iterative reconstruction (ASIR) on CT imaging quality, diagnostic interpretability, and radiation dose reduction for a proven CT acquisition protocol for total body trauma. Methods. 18 patients with multiple trauma (ISS ≥ 16) were examined either with a routine protocol (n = 6), 30% (n = 6), or 40% (n = 6) of iterative reconstruction (IR) modification in the raw data domain of the routine protocol (140 kV, collimation: 40, noise index: 15). Study groups were matched by scan range and maximal abdominal diameter. Image noise was quantitatively measured. Image contrast, image noise, and overall interpretability were evaluated by two experienced and blinded readers. The amount of radiation dose reductions was evaluated. Results. No statistically significant differences between routine and IR protocols regarding image noise, contrast, and interpretability were present. Mean effective dose for the routine protocol was 25.3 ± 2.9 mSv, 19.7 ± 5.8 mSv for the IR 30, and 17.5 ± 4.2 mSv for the IR 40 protocol, that is, 22.1% effective dose reduction for IR 30 (P = 0.093) and 30.8% effective dose reduction for IR 40 (P = 0.0203). Conclusions. IR does not reduce study interpretability in total body trauma protocols while providing a significant reduction in effective radiation dose

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd