Disorder-Free Data Are All You Need - Inverse Supervised Learning for Broad-Spectrum Head Disorder Detection

The development of artificial intelligence (AI)-based medical systems heavily relies on the collection and annotation of sufficient data containing disorders. However, the preparation of data with complete disorder types and adequate annotations presents a significant challenge, limiting the diagnostic capabilities of existing AI-based medical systems. This study introduces a novel AI-based system that accurately detects a broad spectrum of disorders without requiring any disorder-containing data.

We obtained a training dataset of 21,429 disorder-free head computed tomography (CT) scans and proposed a learning algorithm called inverse supervised learning (ISL). This algorithm learns and understands disorder-free samples instead of disorder-contained ones, enabling the identification of all types of disorders. We also developed a diagnosis and visualization software for clinical usage on the basis of the system's ability to provide visually understandable clues.

The system achieved area under the receiver operating characteristic curve (AUC) values of 0.883, 0.868, and 0.866 on retrospective (127 disorder types, 9967 scans), prospective (117 disorder types, 3054 scans), and cross-center (46 disorder types, 554 scans) datasets, respectively. These results demonstrate that the system can detect far more disorder types than previous AI-based systems. Furthermore, the ISL-based systems achieved AUC values of 0.893 and 0.895 on pulmonary CT and retinal optical coherence tomography, respectively, demonstrating that ISL can generalize well to nonhead and non-CT images.

Our novel AI-based system utilizing ISL can accurately and broadly detect disorders without requiring disorder-containing data. This system not only outperforms previous AI-based systems in terms of disorder detection but also provides visually understandable clues, enhancing its clinical utility. The successful application of ISL to nonhead and non-CT images further demonstrates its potential for broad-spectrum medical applications. (Funded by the National Key R&D Program of China and the National Natural Science Foundation of China.).