ABSTRACT
The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79-0.89) and 0.83 (CI 0.78-0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67-0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.
ABSTRACT
It has been over four decades since the launch of the National Cancer Control Programme in India, yet the cancer screening rates for oral cancer remain unremarkable. Moreover, India is bracing a large burden of oral cancer with poor survival rates. An effective public health programme implementation relies on a multitude of factors related to cost-effective evidence-based interventions, the healthcare delivery system, public health human resource management, community behaviour, partnership with stakeholders, identifying opportunities and political commitment. In this context, we discuss the various challenges in the early detection of oral premalignant and malignant lesions and potential solutions.
Subject(s)
Mouth Neoplasms , Precancerous Conditions , Humans , Early Detection of Cancer , IndiaABSTRACT
BACKGROUND: Intraventricular hemorrhage, necrotizing enterocolitis, and bronchopulmonary dysplasia remain significant causes of morbidity and mortality in preterm newborns. OBJECTIVES: Our goal was to assess the familial and genetic susceptibility to intraventricular hemorrhage, necrotizing enterocolitis, and bronchopulmonary dysplasia. METHODS: Mixed-effects logistic-regression and latent variable probit model analysis were used to assess the contribution of several covariates in a multicenter retrospective study of 450 twin pairs born at < or =32 weeks of gestation. To determine the genetic contribution, concordance rates in a subset of 252 monozygotic and dizygotic twin pairs were compared. RESULTS: The study population had a mean gestational age of 29 weeks and birth weight of 1286 g. After controlling for effects of covariates, the twin data showed that 41.3%, 51.9%, and 65.2%, respectively, of the variances in liability for intraventricular hemorrhage, necrotizing enterocolitis, and bronchopulmonary dysplasia could be accounted for by genetic and shared environmental factors. Among the 63 monozygotic twin pairs, the observed concordance for bronchopulmonary dysplasia was significantly higher than the expected concordance; 12 of 18 monozygotic twin pairs with > or =1 affected member had both members affected versus 3.69 expected. After controlling for covariates, genetic factors accounted for 53% of the variance in liability for bronchopulmonary dysplasia. CONCLUSIONS: Twin analyses show that intraventricular hemorrhage, necrotizing enterocolitis, and bronchopulmonary dysplasia are familial in origin. These data demonstrate, for the first time, the significant genetic susceptibility for bronchopulmonary dysplasia in preterm infants.
