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1.
Cancers (Basel) ; 15(16)2023 Aug 16.
Article in English | MEDLINE | ID: mdl-37627148

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.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-20248524

ABSTRACT

The clinical course of coronavirus disease 2019 (COVID-19) infection is highly variable with the vast majority recovering uneventfully but a small fraction progressing to severe disease and death. Appropriate and timely supportive care can reduce mortality and it is critical to evolve better patient risk stratification based on simple clinical data, so as to perform effective triage during strains on the healthcare infrastructure. This study presents risk stratification and mortality prediction models based on usual clinical data from 544 COVID-19 patients from New Delhi, India using machine learning methods. An XGboost classifier yielded the best performance on risk stratification (F1 score of 0.81). A logistic regression model yielded the best performance on mortality prediction (F1 score of 0.71). Significant biomarkers for predicting risk and mortality were identified. Examination of the data in comparison to a similar dataset with a Wuhan cohort of 375 patients was undertaken to understand the much lower mortality rates in India and the possible reasons thereof. The comparison indicated higher survival rate in the Delhi cohort even when patients had similar parameters as the Wuhan patients who died. Steroid administration was very frequent in Delhi patients, especially in surviving patients whose biomarkers indicated severe disease. This study helps in identifying the high-risk patient population and suggests treatment protocols that may be useful in countries with high mortality rates.

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