Hayley V McMahon, Leslie Serrano, Teja Vyavahare, Indya Hairston, Sequoia Ayala, Zainab Jah, Tiffany Hailstorks, Dázon D Diallo, Elizabeth A Mosley
My Health in My Hands: Improving Medication Abortion Knowledge and Closing Disparities with a Community-Led Media Intervention
Health equity
oral cancer remains major worldwide health concern sophisticated diagnostic tools needed aid early diagnosis noninvasive methods like exfoliative cytology albeit help artificial intelligence ai drawn additional interest study aimed harness power machine learning algorithms automated analysis nuclear parameters oral exfoliative cytology analysis two different ai systems namely convoluted neural networks cnn support vector machine svm compared accuracy comparative diagnostic study performed two groups patients n60 control group without evidence lesions n30 group clinically suspicious oral malignancy n30 evaluated patients underwent cytological smears using exfoliative cytology brush followed routine hematoxylin eosin staining image preprocessing data splitting machine learning model development feature extraction model evaluation done independent ttest run nuclear characteristic pearsons correlation coefficient test performed statistical package social sciences spss software ibm spss statistics windows version 280 ibm corp armonk ny usa study found substantial variations study control groups nuclear size p005 nuclear shape p001 chromatin distribution p0001 pearson correlation coefficient svm 06472 cnn 07790 showing svm accuracy availability multidimensional datasets combined breakthroughs highperformance computers new deeplearning architectures resulted explosion ai use numerous areas oncology research discerned diagnostic accuracy exhibited svm cnn models suggests prospective improvements early detection rates potentially improving patient outcomes enhancing healthcare practices