Background ECG data quality can be affected by lead misplacement which can affect clinical decisions. VI and V2 are commonly misplaced in the superior direction from their correct position, which can mimic or conceal abnormalities. The aim of the current study is to use artificial intelligence (AI) in the form of deep learning to detect VI and V2 lead misplacement to enhance ECG data quality and to make the black box decisions of AI systems more transparent by providing AI attention maps. Methods VI and V2 signals were collected from 453 patients (normal n = 151, Left Ventricular Hypertrophy (LVH) n = 151, Myocardial Infarction n = 151) and extracted using a high-resolution body surface potential maps (BSPM) and converted into RGB images. A deep convolutional neural network (CNN) with 68 layers was developed and trained to classify the ECG images of VI and V2 into correct and incorrect placement. An attention map was generated and analyzed for each ECG image in the last convolution layer to show the most important features (see Fig. 1) that have been selected by the CNN. CNN has been trained on 67% of the data and tested on 33%. Results Using CNN with 68 layers, the accuracy of detecting lead misplacement was 92.6% (TN = 291/300, TP = 265/300, FP = 9/ 300, FN = 35/300). Based on attention maps, P waves (56%), T waves (55%) and R (48%) waves contributed the most to the predicted classes correct and incorrect. The S wave was not considered important in most cases in detecting correct VI and V2 placement. The other features, including the PR interval, Q, wave and J point contributed 29%, 17% and 27% respectively to the predicted classes correct and incorrect. Conclusions Deep CNN achieved a high accuracy (92.6%) to detect VI and V2 lead misplacement, whilst increasing the transparency of the algorithmic decision making. Attention maps demonstrate what the algorithm 'looked at' prior to making it's decision, which also elucidate areas of the ECG that are most important in detecting lead misplacement. Physicians can use the attention map to calibrate their trust with the machine and to consider the machine's attention (a proxy for machine rationale). According to the generated attention maps, the P waves, T waves and R waves were considered the most important features, while the S wave was considered as the least important feature. Whilst the other features PR interval, Qwave and J point are considered as mid-level features.