# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 4 Discussion
By integrating these data with information from existing research, a prediction can be made in the form of a statistical confidence interval, which indicates the probability of a potential complication. For this reason, it is imperative to accurately segment the flap so that the algorithm can produce reliable results. The neural network model proposed manages to accomplish this task with an accuracy of 0.87, a specificity of 0.89, and a sensitivity of 0.85, which are values that represent an improvement compared to other existing U-Net models cited in the literature for the segmentation of medical images. Nevertheless, a sensitivity and specificity of at least 0.90 are desirable to minimize the risk of false positives and false negatives, thus ensuring a reliable and accurate diagnosis [60]. Achieving this is only possible if we address the inherent limitations of neural networks, which are also applicable to this particular case. Doing so will facilitate the identification of potential solutions to these limitations. Neural networks demand large amounts of high-quality and meticulously labeled data for training [61]. Collecting and interpreting such data typically requires a significant amount of time and resources, and may raise privacy concerns in the context of medical imaging [62]. The performance of neural networks heavily depends upon the quality and representativeness of the training data [63]. If the dataset is homogeneous, it means that the majority of patients have similar characteristics, which can result in the model's limited performance when applied to a diverse patient population [64]. In the context of flap surgery, this could mean a bias towards certain flap types, patient demographics, or surgical techniques. In selecting the cases for our database, we aimed to maximize diversity and capture a wide range of flap variations. Thus, no two flaps of the same type were included in the training data. Nevertheless, the limited number of cases, gathered only from a single medical institution and conducted by a small group of surgeons, brings up possible limitations of the algorithm when faced with novel surgical situations. To overcome this problem, it is necessary to corroborate data from several medical institutions in order to guarantee the uniqueness and complexity of the database. Another advanced method to augment the dataset, which we have started working on, is represented by a GAN model, which generates synthetic data inspired from the initial database used for training purposes only to refine the results given by the algorithm. Our new approach not only improves the precision of monitoring flaps but also decreases the need for more intrusive procedures, thus enhancing patient comfort and outcomes. Moreover, the incorporation of precise visual analysis into surgical procedures highlights the increasing significance of AI within modern medicine, especially in specialized fields such as plastic surgery that demand accuracy and careful supervision [65]. While these results are encouraging, further studies are needed to validate the reliability and efficacy of this DL-based system across various surgical scenarios and patient populations [66].