A Bayesian approach to clinical trial designs in dermatology with multiple simultaneous treatments per subject and multiple raters.

We consider the statistical analysis of clinical trial designs with multiple simultaneous treatments per subject and multiple raters. The work is motivated by a clinical research project in dermatology where different hair removal techniques were assessed based on a within-subject comparison. We assume that clinical outcomes are assessed by multiple raters as continuous or categorical scores, e.g. based on images, comparing two treatments on the subject-level in a pairwise manner. In this setting, a network of evidence on relative treatment effects is generated, which bears strong similarities to the data underlying a network meta-analysis of clinical trials. We therefore build on established methodology for complex evidence synthesis and propose a Bayesian approach to estimate relative treatment effects and to rank the treatments. The approach is, in principle, applicable to situations with any number of treatment arms and/or raters. As a major advantage, all available data is brought into a network and analyzed in one single model, which ensures consistent results among the treatment comparisons. We obtain operating characteristics via simulation and illustrate the method with a real clinical trial example.

Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists.

BACKGROUND: The clinical diagnosis of face and scalp lesions (FSL) is challenging due to overlapping features. Dermatologists encountering diagnostically 'unclear' lesions may benefit from artificial intelligence support via convolutional neural networks (CNN). METHODS: In a web-based classification task, dermatologists (n = 64) diagnosed a convenience sample of 100 FSL as 'benign', 'malignant', or 'unclear' and indicated their management decisions ('no action', 'follow-up', 'treatment/excision'). A market-approved CNN (Moleanalyzer-Pro®, FotoFinder Systems, Germany) was applied for binary classifications (benign/malignant) of dermoscopic images. RESULTS: After reviewing one dermoscopic image per case, dermatologists labelled 562 of 6400 diagnoses (8.8%) as 'unclear' and mostly managed these by follow-up examinations (57.3%, n = 322) or excisions (42.5%, n = 239). Management was incorrect in 58.8% of 291 truly malignant cases (171 'follow-up' or 'no action') and 43.9% of 271 truly benign cases (119 'excision'). Accepting CNN classifications in unclear cases would have reduced false management decisions to 4.1% in truly malignant and 31.7% in truly benign lesions (both p < 0.01). After receiving full case information 239 diagnoses (3.7%) remained 'unclear' to dermatologists, now triggering more excisions (72.0%) than follow-up examinations (28.0%). These management decisions were incorrect in 32.8% of 116 truly malignant cases and 76.4% of 123 truly benign cases. Accepting CNN classifications would have reduced false management decisions to 6.9% in truly malignant lesions and to 38.2% in truly benign cases (both p < 0.01). CONCLUSIONS: Dermatologists mostly managed diagnostically 'unclear' FSL by treatment/excision or follow-up examination. Following CNN classifications as guidance in unclear cases seems suitable to significantly reduce incorrect decisions.