An annotation driven rule-based algorithm for suggesting multiple 12-lead ECG interpretations

The 12-lead Electrocardiogram (ECG) is ubiquitously used as a diagnostic support tool to detect cardiovascular disease. However, it is difficult to read and is often incorrectly interpreted. This study aims to further previous research, which used of a set of interactive questions and prompts to guide an interpreter through the ECG reporting process. The model was named ‘Interactive Progressive based ECG Interpretation’ (IPI). In this study, the IPI model has been augmented with an automatic diagnoses suggestion tool following annotated analysis of an ECG. To accomplish this, a rule-based algorithm has been created to assess the interpreters' ECG annotations to each of the interactive questions in the IPI model. This Differential Diagnoses Algorithm (DDA) was implemented using web technologies such as JavaScript and uses a modern device agnostic and language independent storage format (JSON) for defining the rules. Hence, by augmenting the IPI model with the DDA we hypothesize that this will further lower the number of interpretation errors and increase diagnostic accuracy in ECG interpretation.