Table 2 Characteristics of five studies on prediction, diagnosis and prognosis of periprosthetic joint infection
First author [Ref.] | Articles | Journal | Year | Number |
|---|---|---|---|---|
Prediction | ||||
 Yeo, I. [7] | The use of artificial neural networks for the prediction of surgical site infection following total knee arthroplasty | The Journal of Knee Surgery | 2022 | 10021 patients |
Diagnosis | ||||
 Kuo, F.C. [8] | Periprosthetic joint infection prediction via machine learning: comprehensible personalized decision support for diagnosis | The Journal of Arthroplasty | 2021 | 323 patients |
 Tao, Y. [13] | A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of periprosthetic joint infection | Arthroplasty | 2022 | 20 patients (Training sets: 461 positive images, 461 negative images) |
Prognosis | ||||
 Shohat, N. [20] | 2020 Frank Stinchfield Award: identifying who will fail following irrigation and debridement for prosthetic joint infection | The Bone & Joint Journal | 2020 | 609 patients |
 Klemt, C. [21] | Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection | Knee Surgery, Sports Traumatology, Arthroscopy | 2021 | 618 patients |