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's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's 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' 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's 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 (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, 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's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Quantification of the vasodilatory effect of axillary plexus block. A prospective controlled study.

BACKGROUND: Axillary plexus block is a common method for regional anesthesia, especially in hand and wrist surgery. Local anesthetics (e.g., mepivacaine) are injected around the peripheral nerves in the axilla. A vasodilatory effect due to sympathicolysis has been described, but not quantified. MATERIALS AND METHODS: In a prospective controlled study between October 2012 and July 2013, we analyzed 20 patients with saddle joint arthritis undergoing trapeziectomy under axillary plexus block. Patients received a mixture of mepivacaine 1% and ropivacaine 0.75% in a 3:1 ratio. The measurements were carried out on the plexus side and the contralateral hand, which acted as the control. Laser-Doppler spectrophotometry (oxygen to see [O2C] device) was used to measure various perfusion factors before and after the plexus block, after surgery and in 2-h intervals until 6 h postoperatively. RESULTS: Compared with the contralateral side, the plexus block produced an enhancement of tissue oxygen saturation of 117.35 ± 34.99% (cf. control SO2: 92.92 ± 22.30%, P < 0.010) of the baseline value. Furthermore, blood filling of microvessels (rHb: 131.36 ± 48.64% versus 109.12 ± 33.25%, P < 0.0062), peripheral blood flow (219.85 ± 165.59% versus 129.55 ± 77.12%, P < 0.018), and velocity (163.86 ± 58.18% versus 117.16 ± 45.05%, P < 0.006) showed an increase of values. CONCLUSIONS: Axillary plexus block produces an improvement of peripheral tissue oxygen saturation of the upper extremity over the first 4 h after the inception of anesthesia.