## 3 Results
### Pathology  
Several studies assessed the ability of AI to help pathologists in PCa identification and evaluation, with a gain in time and reduction of inter-observer variability (Table 2). [PERSON] et al. [44] showed that a ML algorithm reported good accuracy in diagnosing (90.5%) or classifying (79.2%) PCa compared to pathologists. Working speed of the machine allowed the analysis of hundreds of slides in a few hours. With different AI models, [45; 46; 47] found identical results with a good concordance between pathologist and a CNN model, with high accuracy, sensibility, and specificity in diagnosing and differentiating low and high-grade PCa. [PERSON] et al. [48] provided additional information. Once again, kappa score between CNN and pathologists was identical to that between pathologists. However, interestingly, when there was a disagreement with pathologists, the stratification made by the CNN model improved prediction regarding disease-specific survival outcomes. [PERSON] et al. [49] compared the pre-operative prediction of advanced PCa by using and comparing an ANN algorithm and a SVM model. Both have good results in predicting the pre-operative probability for > pT3a PCa, but SVM slightly outperformed ANN in this study. Partin tables are used to estimate the distribution of the risk of pathological stages. The Partin tables use clinical features, such as Gleason score, PSA, and clinical stage, to predict whether the tumor will be confined to the organ. [PERSON] et al. [50] compared the performance of these nomogram tables to an ANN model. Their ANN algorithm, STATISTICA, trained with total serum PSA, TNM stage, and biopsy Gleason score, had a significantly higher efficiency than Partin tables. Two clinical variables were significantly associated with a non-organ-confined disease: PSA and BMI. [PERSON] et al. [51] optimized Partin tables by combining conventional parameters with those from a SMV model. This model significantly outperformed conventional Partin tables for organ-confined PCa (AUC = 0.891 vs. 0.730). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**AI Model**} & \multicolumn{2}{c}{**Patients**} & \multicolumn{2}{c}{**Parameters**} & \multicolumn{1}{c}{**Predicted Outcomes**} \\ \cline{4-5}  & & **(Training/Validation/Testing)** & & & \\ \hline \multirow{5}{*}{[PERSON] et al. Med Image} & Supervised ML algorithm & 287 RAP (201/56) with 563 & 23 weighted histological features on TMA cores & The classifier’s accuracy in detecting cancer was 90.8\%. For the distinction between NS and SC PCa, the accuracy was 79.2\%, with 79.2\% and 79.1\% of sensitivity and specificity, respectively. \\  & (U-Net) & (333/20) TMA cores & features on TMA cores & The manual grading by pathologists leads to inter-observer mean accuracy of 97.2\%, with 89.4\% and 86.2\%. of sensitivity and specificity respectively for cancer detection, and 78.8\%.9\%, and 79.3\%, respectively. for NS vs. SC PCa. ML’s working time was 18.5h for the training and validation steps. \\ \hline \multirow{5}{*}{[PERSON] et al. IEEE Trans Med Imaging [45]} & ML algorithm & \begin{tabular}{c} 15 RAP with 991 \\ sub-images extracted from \\ digital pathology images of \\ 50 whole-mount tissue \\ sections \\ \end{tabular}  & \begin{tabular}{c} The inter-annotator agreement between ML model and pathologist was \(k=0.51\), while the overall agreements between the 6 pathologists was 0.8\% to 0.62 (0.5 \(\pm\) 0.07) \\ \hline \multirow{5}{*}{[PERSON] et al. Med Image Anal 2018 [44]} & Supervised ML algorithm & 287 RAP (201/56) with 563 & 23 weighted histological features on TMA cores & The classifier’s accuracy in detecting cancer was 90.8\%. For the distinction between NS and SC PCa, the accuracy was 79.2\%, with 79.2\% and 79.1\% of sensitivity and specificity, respectively. \\  & (U-Net) & (333/20) TMA cores & features on TMA cores & The manual grading by pathologists leads to inter-observer mean accuracy of 97.2\%, with 89.4\% and 86.2\%.