Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge

Abstract

Background: Previous studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.

Methods: We designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25 331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.

Findings: 64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.

Interpretation: We have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.

Funding: Melanoma Research Alliance and La Marató de TV3.

Figure 1:. Algorithm accuracy across all submissions, by dataset, metadata use, and diagnostic class

(A) Boxplot and table showing median (IQR) for balanced accuracy across all participant submissions for each test set partition (p<0·001 for all comparisons). (B) Boxplot of diagnosis-specific balanced accuracies for each diagnostic class. (C) Comparison of balanced accuracy over all submissions with and without clinical metadata. AK=actinic keratosis. BCC=basal cell carcinoma. BCN=Hospital Clinic Barcelona. BKL=benign keratosis. DF=dermatofibroma. HAM=Medical University of Vienna. MEL=melanoma. NT=not trained. NV=nevi. SCC=squamous cell carcinoma. VASC=vascular lesions.

Figure 2:. Confusion matrix, separated into nine groups for each diagnostic category in the test set

Values represent the proportion of images in the test set given a classification specified by columns, on average for the top 25 algorithms. The reference row of each group shows the aggregate values for each diagnosis. Subsequent rows include stratifications across artifacts (ie, crust, hair, pen marks), anatomical site, and source institution. Upper extremity refers to arms and feet (not palms or soles). Lower extremity refers to legs (not palms or soles). AK=actinic keratosis. BCC=basal cell carcinoma. BCN=Hospital Clinic Barcelona. BKL=benign keratosis. DF=dermatofibroma. HAM=Medical University of Vienna. MEL=melanoma. NT=not trained. NV=nevi. SCC=squamous cell carcinoma. VASC=vascular lesion.

Figure 2:. Confusion matrix, separated into nine groups for each diagnostic category in the test set

Values represent the proportion of images in the test set given a classification specified by columns, on average for the top 25 algorithms. The reference row of each group shows the aggregate values for each diagnosis. Subsequent rows include stratifications across artifacts (ie, crust, hair, pen marks), anatomical site, and source institution. Upper extremity refers to arms and feet (not palms or soles). Lower extremity refers to legs (not palms or soles). AK=actinic keratosis. BCC=basal cell carcinoma. BCN=Hospital Clinic Barcelona. BKL=benign keratosis. DF=dermatofibroma. HAM=Medical University of Vienna. MEL=melanoma. NT=not trained. NV=nevi. SCC=squamous cell carcinoma. VASC=vascular lesion.

Figure 3:. Confusion matrix of the diagnoses comprising the NT category

The confusion matrix shows which of the other categories included in training the diagnoses were confused for, measured across the top 25 algorithms. AK=actinic keratosis. BCC=basal cell carcinoma. BKL=benign keratosis. DF=dermatofibroma. MEL=melanoma. NT=not trained. NV=nevi. SCC=squamous cell carcinoma. VASC=vascular lesion.

Figure 4:. Receiver operating characteristic curves for the expert readers on grouped malignant diagnoses (A) and NT class (B) as compared with the top three algorithms

Crosses represent the average sensitivity and specificity of the readers, with the length of the bars corresponding to the 95% CI. AI=artificial intelligence. NT=not trained. SROC=summary receiver operating characteristic curve.