Diagnosing orthopaedic infection by identifying neutrophils in whole histology slide images with machine learning trained on publicly available datasets.

Aims: This study examines the ability of YOLO (You Only Look Once) 11x, a widely used and state of the art object detection model, trained on publicly available datasets, to identify and count neutrophils in tissue samples taken at prosthetic joint revision surgery, with the objective of automating a laborious but necessary part of the diagnostic workup for periprosthetic joint infection. Methods: Three datasets containing blood film microscopic slides with neutrophils were downloaded, combined, and labelled. The resulting dataset of 3,923 images was augmented with ten additional histological slides from periprosthetic tissue, taken at the time of revision surgery (5 infected, 5 sterile), and split into training (70%), validation (20%), and test (10%) sets. The dataset was used to train YOLO 11x object detection model optimized for a mean average precision above 50%. The trained network was tested on a ground truth specimen and histological whole slide images from 19 additional cases, previously unseen by the model, for validation. The threshold for diagnosis of infection on histological sections was set at more than five neutrophils per 0.2 mm Results: The model performed well as ground truth image returned precision at 82%, recall (sensitivity) 79%, and F1 harmonic mean 80%. When assessed against formal histopathological, microbiological, and multidisciplinary team (MDT) diagnosis, precision was 78%, 80%, and 90%; recall 78%, 89%, and 82%; and F1 score 78%, 84%, and 86%, respectively. Against the definitive MDT diagnosis, our model identified nine out of the ten infected cases and excluded seven out of nine cases that were not infected. Conclusion: This study demonstrates ability of the trained model to identify neutrophils in tissue taken at revision surgery and could assist in diagnosis of periprosthetic infection. Further work is needed to improve confidence in the identifications and diagnostic accuracy of periprosthetic infection.