RESUMO
In computer-aided medical diagnosis, deep learning techniques have shown that it is possible to offer performance similar to that of experienced medical specialists in the diagnosis of knee osteoarthritis. In this study, a new deep learning (DL) software, called "MedKnee" is developed to assist physicians in the diagnosis process of knee osteoarthritis according to the Kellgren and Lawrence (KL) score. To accomplish this task, 5000 knee X-ray images obtained from the Osteoarthritis Initiative public dataset (OAI) were divided into train, valid, and test datasets in a ratio of 7:1:2 with a balanced distribution across each KL grade. The pre-trained Xception model is used for transfer learning and then deployed in a Graphical User Interface (GUI) developed with Tkinter and Python. The suggested software was validated on an external public database, Medical Expert, and compared with a rheumatologist's diagnosis on a local database, with the involvement of a radiologist for arbitration. The MedKnee achieved an accuracy of 95.36% when tested on Medical Expert-I and 94.94% on Medical Expert-II. In the local dataset, the developed tool and the rheumatologist agreed on 23 images out of 30 images (74%). The MedKnee's satisfactory performance makes it an effective assistant for doctors in the assessment of knee osteoarthritis.
RESUMO
Traumatic dislocation of the testicle is a rare complication that occurs after blunt trauma to the scrotum or abdominopelvic injury. The majority of cases occur in young adults following severe scrotal trauma in a motorcycle accident. We report the case of a 1.5-year-old infant admitted for left scrotal swelling with a left inguinal mass. Ultrasound and Doppler examination showed a twisted necrotic testicle with inguinal adenopathy. Surgical exploration in the emergency room confirmed that the inguinal mass was a dislocated testicle with an intrascrotal hematoma that was evacuated followed by orchidopexy of the dislocated testicle.
