Diagnosing ligament injuries using MRI scans is a labor-intensive task that requires an expert. In this paper, we propose a fully recurrent neural network (RNN) for detecting Anterior Cruciate Ligament (ACL) tears using MRI scans. The proposed network localizes the ACL and classifies it into several categories: ACL tear, normal tear, and healthy. Existing detection methods use deep learning networks based on single MRI sections, and in this way lose 3D spatial context. To address this, we propose a fully recurrent neural network that processes a sequence of 3D sections and so captures 3D spatial context. The proposed network is based on a YOLOv3 backbone and can produce a sequence of decisions which are then combined by majority voting. Experimental results show improvement over state-of-the-art methods.