Posterior wall fractures are the most common type of acetabular fractures. Treatment can be conservative or surgical. Operative treatment is indicated for acetabular fractures that result in hip joint instability and/or incongruity, as well injuries with incarceration of fragments of bone or soft tissue within the hip joint. Surgical treatment can range from open reduction and osteosynthesis to hip arthroplasty. Arthroscopy has recently been used as the main surgical technique or as a reduction aid. In this case a 26-year-old male with a fracture of the posterior wall who underwent a posterior miniinvasive approach, followed by hip arthroscopy. This allowed joint wash, removal of the loose body, confirmation of reduction and absence of intra-articular hardware. Excellent clinical and radiological results were obtained. This case demonstrates the advantage of using hip arthroscopy in assessment of fracture reduction, the absence of intra-articular hardware or fragments, as well as a less invasive approach.

Background: Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration. Methods: We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio. Results: This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value. Conclusions The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.