Abstract
Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding-and no deep learning-expertise. We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit Library dataset. Diagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (sensitivity 73·3-97·0%; specificity 67-100%; AUPRC 0·87-1·00). In the multiple classification tasks, the diagnostic properties ranged from 38% to 100% for sensitivity and from 67% to 100% for specificity. The discriminative performance in terms of AUPRC ranged from 0·57 to 1·00 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0·47, with a sensitivity of 49% and a positive predictive value of 52%. All models, except the automated deep learning model trained on the multilabel classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The quality of the open-access datasets (including insufficient information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitations of this study. The availability of automated deep learning platforms provide an opportunity for the medical community to enhance their understanding in model development and evaluation. Although the derivation of classification models without requiring a deep understanding of the mathematical, statistical, and programming principles is attractive, comparable performance to expertly designed models is limited to more elementary classification tasks. Furthermore, care should be placed in adhering to ethical principles when using these automated models to avoid discrimination and causing harm. Future studies should compare several application programming interfaces on thoroughly curated datasets. National Institute for Health Research and Moorfields Eye Charity.
Highlights
Diagnosis, an understanding of the probability for presence of illness, depends on data: its collection, integration, and interpretation enables accurate classification of clinical presentations into an accepted disease category
Evidence before this study We did a systematic search of the literature to identify classical deep learning models to provide a comparison to models constructed using the automated deep learning platform Google Cloud AutoML
In an effort to emulate external validation in one exemplary use-case, the authors used the dermatology image set for training and tuning of a deep learning model and tested its performance by using a separate skin lesion image dataset, the Edinburgh Dermofit Library.[24]
Summary
An understanding of the probability for presence of illness, depends on data: its collection, integration, and interpretation enables accurate classification of clinical presentations into an accepted disease category. Human diagnosticians achieve acceptable accuracy in such classification tasks through the learning of diagnostic rules (patterns recorded by other human diagnosticians) followed by training on real cases for which the diagnostic labels are provided (supervised clinical experience). Evidence before this study We did a systematic search of the literature to identify classical deep learning models (with bespoke architectures developed by human experts) to provide a comparison to models constructed using the automated deep learning platform Google Cloud AutoML. Studies were included if the authors developed a deep learning algorithm on the datasets used in their study. The search strategy is available in the appendix. The performance of these existing models served as a direct comparison to the model we developed
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