ABSTRACT
PURPOSE: This study aims to evaluate the effectiveness of advanced deep learning models, specifically YOLOv8 and EfficientNetV2, in detecting meniscal tears on magnetic resonance imaging (MRI) using a relatively small data set. METHOD: Our data set consisted of MRI studies from 642 knees-two orthopaedic surgeons labelled and annotated the MR images. The training pipeline included MRI scans of these knees. It was divided into two stages: initially, a deep learning algorithm called YOLO was employed to identify the meniscus location, and subsequently, the EfficientNetV2 deep learning architecture was utilized to detect meniscal tears. A concise report indicating the location and detection of a torn meniscus is provided at the end. RESULT: The YOLOv8 model achieved mean average precision at 50% threshold (mAP@50) scores of 0.98 in the sagittal view and 0.985 in the coronal view. Similarly, the EfficientNetV2 model obtained area under the curve scores of 0.97 and 0.98 in the sagittal and coronal views, respectively. These outstanding results demonstrate exceptional performance in meniscus localization and tear detection. CONCLUSION: Despite a relatively small data set, state-of-the-art models like YOLOv8 and EfficientNetV2 yielded promising results. This artificial intelligence system enhances meniscal injury diagnosis by generating instant structured reports, facilitating faster image interpretation and reducing physician workload. LEVEL OF EVIDENCE: Level III.
ABSTRACT
Inguinal hernia is the most common seen groin hernias which mostly contain bowel. The incidence of vermiform appendix in an inguinal hernia is seen in 1% of all inguinal hernia. This is known as Amyand's hernia. Appendix within a hernia can be normal or complicated by appendicitis. Most of these cases are not diagnosed preoperatively and managed during surgery. Preoperative diagnosis of these cases is so rare. Very few cases have been reported so far. In our case, we diagnosed an inflamed appendix in a 49 years old female within right inguinal hernia by using ultrasound and confirmed it by CT scan.