## 4 Diagnosing with AI
### Knee Related Diagnosis  
[PERSON] et al. [19] constructed a CNN that had to recognize Tibial Plateau Fracture (TPF) from X-ray images and compared its performances with respect to expert orthopedic physicians, using as indicators accuracy and time spent on analysis. The algorithm was trained on a set of 916 JPEG files (training data set) and then tested on a set of 84 JPEG files (test data set) for algorithm validation. During the test, the algorithm had to label with a rectangle suspected fracture areas. The results were promising: the difference in accuracy of the machine with respect to the human radiologists (0.91 vs. 0.92 \(\pm\) 0.03) was not statistically significant, while the time spent on average on each sample by the machine was 16 times faster than the one required by humans (0.55 vs. 8.44 \(\pm\) 3.26 s), and, more importantly, it is to be noted that the physicians were tested in a stress-free environment, which is rarely the case in a real life emergency department. This factor was indeed one of the reasons leading the authors of such a study to believe that the performance of their AI algorithm would be even better in the real clinical environment [19]. However, despite the excellent results, the trained algorithm could only recognize the fracture line without classifying it; this function, as the authors stated, will be added to the algorithm in the future. The other limitations found in this study were related to the fact that no normal knee radiographs were present in the data set, thus precluding the possibility of computing sensitivity and specificity. Additionally, the usage of only anteroposterior films may not be enough for a correct diagnosis of a TPF. Another important injury related to the TPF that has become extremely common is the one involving the menisci, which are extremely vulnerable to injury due to their position and function in the knee joint [20]. The diagnosis of this type of injury is more complicated due to the presence of both bone structure and soft tissue. One of the many methods used for diagnosing is the magnetic resonance imaging (MRI), which has the advantage of being noninvasive and having high resolution for the soft tissues; the problem is, however, that the image resulting from the test would often appear blurred because of micro-movements of the limbs due to the patient, the breathing and/or the heartbeat [20]. In order to avoid these complications, [PERSON] et al. [20] thought about using a machine learning approach for MRI image improvement: by using, in the process of MRI image reconstruction, a CNN-based algorithm, they removed \"shadowy\" parts and reduced blurriness in the under sampled images, giving then as output fully sampled cleaner MRI images. These images were then used for diagnosing and were later confirmed during the surgery itself. The results showed an accuracy of 95.3% and a sensitivity of 96.9%, meaning that this CNN-based MRI optimizing algorithm could be considered effective for the diagnosis of tibial plateau fracture with meniscus injury, but the sample size used has to be considered, as the data set is not big enough to strengthen the results of the study. A different application of AI was developed by [PERSON] et al. [21], who trained a CNN to recognize different total knee implant models, information that can become vital during preoperative planning before partial revision surgery (where only one or two components of the implant have to be changed). In this case the task to perform is different, and quite fitting for AI: while humans with good experience can generalize quite well characteristics of different objects/situations/conditions from different fields of knowledge and can recognize them with a high accuracy, they cannot process large amount of data like machines do. That is the case of the data of the implant's producer, model and general characteristics.