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.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.

The recursive search motion estimation algorithm offers smooth and accurate motion vector fields. Computationally, the most expensive part of the motion estimator is the evaluation of the various motion vector candidates. Evaluation is performed by comparing blocks in two consecutive frames indicated by motion vector candidates. The paper addresses the issue of reducing the already extremely low number of motion vector evaluations. We apply preprocessing techniques to reduce the number of motion vector candidates from 7 to 5, i.e. 30%, without sacrificing quality. We exemplify the above findings through experimental results obtained using the 3D recursive search motion estimation algorithm. The required preprocessing overhead is negligible.

PurposeRetrieving images of organs and their associated pathologies is essential for evidence-based clinical diagnosis. Deep neural hashing (DNH) has demonstrated the ability to retrieve images fast on large datasets. Conventional pairwise DNH methods can focus on semantic similarity between either organs or pathology of an image pair but not on both simultaneously.ApproachWe propose an organ and pathology contextual-supervised hashing approach (OPHash) learned using three types of samples (called bags) to learn accurate hash representation. Because only semantic similarity is inadequate to incorporate with these bags, we introduce relational similarity to generate identical hash codes from most similar image pairs. OPHash is trained by minimizing classification loss, two retrieval losses implemented using Cauchy cross-entropy and maximizing discriminator loss over training samples.ResultsExperiments are performed with two radiology datasets derived from the publicly available datasets. OPHash achieves 24% higher mean average precision than the state-of-the-art for top-100 retrieval.ConclusionOPHash retrieves images with semantic similarity of organs and their associated pathology. It is agnostic to image size as well. This method improves retrieval efficiency across diverse medical imaging datasets, accommodating multiple organs and pathologies. The code is available at https://github.com/asimmanna17/OPHash.