Man against Machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Background: Deep learning convolutional neural networks (CNN) May facilitate melanoma detection, but data comparing a CNN\u2019s diagnostic performance to larger groups of dermatologists are lacking. Methods: Google\u2019s Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists\u2019 diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN\u2019s performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (6standard deviation) sensitivity and specificity for lesion classification of 86.6% (69.3%) and 71.3% (611.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (69.6%, P \ubc 0.19) and specificity to 75.7% (611.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN\u2019s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians\u2019 experience, they May benefit from assistance by a CNN\u2019s image classification

Metal ion levels in large-diameter total hip and resurfacing hip arthroplasty-Preliminary results of a prospective five year study after two years of follow-up

<p>Abstract</p> <p>Background</p> <p>Metal-on-metal hip resurfacing is an alternative to metal-on-metal total hip arthroplasty, especially for young and physically active patients. However, wear which might be detected by increased serum ion levels is a matter of concern.</p> <p>Methods</p> <p>The aims of this preliminary study were to determine the raise of metal ion levels at 2-years follow-up in a prospective setting and to evaluate differences between patients with either resurfacing or total hip arthroplasty. Furthermore we investigated if the inclination of the acetabular component and the arc of cover would influence these findings. Therefore, 36 patients were followed prospectively.</p> <p>Results</p> <p>The results showed increments for Co and Cr in both implant groups. Patients treated with large-diameter total hip arthroplasty showed fourfold and threefold, respectively, higher levels for Co and Cr compared to the resurfacing group (Co: p < 0,001 and Cr: p = 0,005). Nevertheless, we observed no significant correlation between serum ion levels, inclination and arc of cover.</p> <p>Discussion</p> <p>In order to clarify the biologic effects of ion dissemination and to identify risks concerning long-term toxicity of metals, the exposure should be monitored carefully. Therefore, long-term studies have to be done to determine adverse effects of Co and Cr following metal-on-metal hip replacement.</p