Concepción Barrio, Dahlia Fuentes, Lize Tibiriçá, Mercedes Hernandez, Paula Helu-Brown, Shahrokh Golshan, Barton W Palmer
Consent for Research Involving Spanish- and English-Speaking Latinx Adults With Schizophrenia
Schizophrenia bulletin
deep learning classifier improves accuracy colposcopic impression colposcopy images taken 56 seconds acetic acid application processed cervix detection algorithm identify cervical region optimized models based segformer architecture classify cervix highgrade negativelowgrade data split histologically stratified random training validation test subsets 801010 replicated 10fold experiment align prior study utilizing expert reviewer analysis images evaluate models robustness across different cameras retrained dividing dataset camera type subsequently retrained model new histologically stratified random data split integrated results patients age referral data train gradient boosted tree model final classification model accuracy assessed receiver operating characteristic area curve auc youdens index yi sensitivity specificity compared histology 5485 colposcopy images 4946 histology visible cervix used models average performance 10fold experiment auc 075 yi 037 sensitivity 63 specificity 74 outperforming experts average yi 016 transferability across camera types effective auc 070 yi 033 integrating imagebased predictions referral data improved outcomes auc 081 yi 046 use model predictions alongside original colposcopic impression boosted overall performance deep learning cervical image classification demonstrated robustness outperformed experts improved including additional patient information shows potential clinical utility complementing colposcopy