# Machine-Learning Applications in Oral Cancer: A Systematic Review
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Year** & **Clinical** & **Applied** & **Input** & **\#** & **ROB** & **Concluding** \\
**Context** & **Context** & **Algorithm** & **Features** & **Samples** & **Remarks** \\ \hline
**[**33**]** & 2018 & Diagnosis and Prevention & CNN, RF & Microscopic images of the oral mucosa & 100 & + & CNN approach is proposed for segmentation of different constituent layers from oral mucosa histology images \\ \hline
**[**34**]** & 2000 & Potentially malignant oral lesions (pre-cancer) & **ANN** & Spectral data & 28 & - & Neural networks provide a very good discrimination between autofluorescence spectra of leukoplakia and normal tissue \\ \hline
**[**35**]** & 2019 & Diagnosis and Prevention & CTA & Medical/dental experience, psychosocial factors, demographics & 2401 & + & Classification tree analysis (CTA) to identify population subgroups that are less likely to have an oral cancer examination (OCE) \\ \hline
**[**36**]** & 2004 & Diagnosis and Prevention & ANN, KILC, PCA & Spectral data & 134 & + & Classification and detection of invisible tissue alterations through autofluorescence spectroscopy applying PCA and ANN methods \\ \hline
**[**37**]** & 2003 & Prognosis & LR, DTREE & Tumor size, mode of invasion, and keratinization & 118 & + & Three statistical methods for the prediction of lymph node metastasis in carcinoma of the tongue are compared \\ \hline
**[**38**]** & 2017 & Diagnosis and Prevention & RF & Histopathological images of OSCC & 150 & + & Automated technique for accomplishing the task of mitotic cell count from related histopathological images \\ \hline
**[**39**]** & 2019 & Diagnosis and Prevention & CNN & Hyperspectral images & 100 & + & Proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis \\ \hline
**[**40**]** & 2019 & Diagnosis and Prevention & CNN & Computed tomography scan images & 441 & + & Deep-learning image classification system for the diagnosis of lymph node metastasis on CT images \\ \hline
**[**41**]** & 2019 & Diagnosis and Prevention & CNN, SVM, LDA & Spectral data & 1440 & + & Classification method that discriminates tongue squamous cell carcinoma from non-tumorous tissue \\ \hline
**[**42**]** & 2018 & Diagnosis and Prevention & SVM & Infrared (IR) thermal imaging & 90 & + & Automatic analysis by an entropy gradient support vector machine (EGSVM) using a digital infrared thermal imaging system \\ \hline
**[**43**]** & 2019 & Prognosis & RF, DJU, LR, ANN & Personal details, tumor, and treatment characteristics & 33,065 & + & Describes a model using machine learning to help predict?-year overall survival among patients with oral squamous cell carcinoma (QSCC) \\ \hline
**[**44**]** & 2015 & \begin{tabular}{c} Potentially malignant oral \\ lesions (pre-cancer) \\ \end{tabular} & \
\begin{tabular}{c} RVM, SVM, MLC \\ \end{tabular} &
\begin{tabular}{c} Images of lip border \\ \end{tabular} & 150 & + & Using robust macro-morphological descriptors of the vermillion border from non-standardized digital photographs with a probabilistic model (RVM) for solarellosis recognition \\ \hline
**[**45**]** & 2015 & Diagnosis and Prevention & SVM & Spectral data & 47 & + & Classification of two oral lesions, namely oral leukoplakia (UK) and oral squamous cell carcinoma (QSCC), was performed with SVM using different combinations of spectral features \\ \hline
**[**46**]** & 2019 & Diagnosis and Prevention & LDA, SVM & Spectral data & 34 &? &? \\ \hline
**[**47**]** & 2019 & Diagnosis and Prevention & LR & Tissue microarray chips & 105 & + &
\begin{tabular}{c} The effectiveness of Aurora kinase A and Ninein interacting protein (AUNIP) in diagnosing OSC was evaluated by machine learning \\ \end{tabular} \\ \hline
**[**48**]** & 2015 & Prognosis & SVM & Protein intensity & 30 & + &
\begin{tabular}{c} Protemore whole saliva and salivary extracellular vesicles (EVs) from patients with OSCC and healthy individuals were analyzed. The proteomics data could classify OSCC with 90% accuracy \\ \end{tabular} \\ \hline
**[**49**]** & 2019 & Diagnosis and Prevention & SVM & \
\begin{tabular}{c} Putrescine, glycosyl-leucine, and phenylalanine \\ \end{tabular} & 31 & + &
\begin{tabular}{c} With three-marker panel, consisting of putrescine, glycosyl-leucine, and phenylalanine using a support vector machine (SVM) model that can discriminate paired cancerous (T) from adjacent noncancerous (AN) tissues \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}