# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 4 Discussion  
In this study, we systematically reviewed the literature and described the state-of-the-art, as well as current, applications of machine learning in oral cancer. In this systematic review, we quantified the chosen studies and classified them according to four areas of application: diagnosis and prevention, prognosis, potentially malignant oral lesions (pre-cancer), and therapy and quality of life. To our knowledge, this is the first systematic review of ML applications in oral cancer. In recent years, many studies have been published by using genomic, histopathological, image, medical/clinical, spectral, and speech data in combination with machine-learning techniques with the aim of applying this knowledge to the four previously mentioned application areas [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73]. The most cited machine-learning algorithms in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. In the area of ANN, it is important to mention the growth of the deep-learning subarea since 2017. This growth can be explained due to the greater availability of data, as well as the greater computing power through architectures that are dedicated to machine learning, and the use of graphic processing units (GPUs) that considerably reduce the processing time of the data [74]. Most of the machine-learning applications were concentrated in the analysis of medical history/clinical data, spectral data, genomic data, and image analysis. With respect to the different clinical contexts analyzed, for diagnosis and prevention, most of the applied algorithms were SVM, ANN, and LR. The best one was SVM due to its AUC value in comparison to ANN and LR. In addition, the ANOVA \(p\)-value was 0.039, showing statistical difference between SVM, ANN, and LR (Table 2). In the case of prognosis, most of the applied algorithms were SVM, ANN, and LR. The best one was ANN due to its AUC value (0.76) in comparison to the other algorithms (Table 2). Nevertheless, SVM is also a good predictor due to its homogenous values among the different metrics of ACC, SE, SP, and AUC (0.75). In this way, it is possible to say that SVM and ANN are the most adequate algorithms in the clinical context of prognosis. According to potentially malignant oral lesions (pre-cancer), most of the applied algorithms were also SVM, ANN, and LR. The best one was SVM; the metrics of ACC, SE, and SP were equal to or greater than 89.69% (Table 2). Finally, for therapy and quality of life, most of the applied algorithms were SVM and ANN. The best one was SVM, with and AUC value of 0.89 (Table 2). The most common machine-learning applications were focused on diagnosis and prevention, followed by prognosis and potentially malignant oral lesions. However, oral cancer is not a particularly difficult malignancy to diagnose. The mouth is a part of the body that is readily accessible for early detection [75]. A great clinical challenge is to establish which lesions will progress to oral cancer. Early diagnosis of oral pre-cancerous lesions is particularly challenging because it requires dentists to be familiar with the range of clinical presentations of potentially malignant oral lesions, many of which may resemble less serious lesions [76]. Here, algorithms have many opportunities to have a clinical impact, for example, in supporting the histopathological evaluation of dysplastic lesions. In this aspect, the applications in potentially malignant oral lesions comprise the 21.05% of the total studies analyzed. Recent studies in this area performed image analysis [21, 60, 61]. Otherwise, despite the promising results, none of the studies demonstrated an improvement in detecting potentially malignant oral lesions.