We developed a deep learning (DL) algorithm to segment scaphoids and detect scaphoid fractures on single-incidence wrist radiographs, the most frequent carpal injuries whose early diagnosis is crucial for wrist function. We exploited a dataset of 1477 wrist radiographs and investigated strategies to mitigate data imbalance caused by low fracture prevalence (9%). The segmentation model achieved excellent precision (mAP@0.75 of 0.98), localising the scaphoid in all but one of 1141 test radiographs. However, severe class imbalance posed challenges in fracture detection. Our best fracture detection model achieved 74% sensitivity and 76% specificity on a balanced test dataset, surpassing an expert musculoskeletal radiologist’s sensitivity of 48% but falling short of their 94% specificity. This study demonstrates the potential of DL to detect scaphoid fractures from single-incidence radiographs, even in highly imbalanced datasets, potentially avoiding misdiagnosis and improving accuracy in emergency settings and non-specialised centres while reducing reliance on additional ionising imaging.