Accurate diagnosis of middle ear diseases, such as acute otitis media (AOM), remains a clinical challenge due to the reliance on subjective visual assessment through otoscopy. While deep learning has shown promise in improving diagnostic accuracy using digital otoscopy videos, existing models often rely on manually selected still frames, a step that reduces their practicality in real-world clinical workflows. In this study, we present the first systematic method (LUCID) for automatically identifying the most informative frame (MIF) selection in otoscopy videos. Through analyzing of 713 videos, we identified three key factors that impact frame informativeness: eardrum visibility, eardrum coverage, and image clarity. We then develop a novel MIF pipeline that integrates (1) a ResNet-50 classifier trained on over 38,000 labeled frames to assess eardrum visibility, (2) a binary-adversarial CAM (BC-AdvCAM) method for weakly supervised eardrum segmentation and coverage estimation, and (3) a specialized blur and focus detection algorithm tailored to otoscope imagery. These components are combined into an "informative score" to rank frames automatically. Comparative evaluations using human reviewers and diagnostic AI models show that frames selected by our AI method perform comparably to expert-selected frames—achieving similar classification accuracy across multiple deep learning architectures. Notably, using the top four frames per video identified by our method significantly improves diagnostic accuracy over using a single expert-selected frame. This framework offers a scalable, expert-level tool for automating key frame selection and enhancing AI-based otoscopy diagnosis. The code is available at : https://github.com/CAIR-LAB-WFUSM/informatic_frame_selction.git

The rising incidence of cancer underscores the imperative for innovative diagnostic and prognostic methodologies. This study delves into the potential of RNA-Seq gene expression data to enhance cancer classification accuracy. Introducing a pioneering approach, we model gene expression data as point clouds, capitalizing on the data's intrinsic properties to bolster classification performance. Utilizing PointNet, a typical technique for processing point cloud data, as our framework's cornerstone, we incorporate inductive biases pertinent to gene expression and pathways. This integration markedly elevates model efficacy, culminating in developing an end-to-end deep learning classifier with an accuracy rate surpassing 99%. Our findings not only illuminate the capabilities of AI-driven models in the realm of oncology but also highlight the criticality of acknowledging biological dataset nuances in model design. This research provides insights into application of deep learning in medical science, setting the stage for further innovation in cancer classification through sophisticated biological data analysis. The source code for our study is accessible at: https://github.com/cialab/GPNet.

Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks. We validated our method effectiveness through a comparative study using a simulated dataset and RNA-Seq data from The Cancer Genome Atlas breast cancer dataset. Our method was benchmarked against traditional techniques (ORA, FCS), shallow machine learning models (logistic regression, support vector machine), and deep learning approaches (DeepHisCom, PASNet). The results demonstrate that GPNet outperforms these methods in low-SNR, large-sample datasets, where it remains robust and reliable, significantly reducing both Type I error and improving power. This makes our method well suited for pathway analysis in large, multi-center studies. The code can be found at https://github.com/haolu123/GPNet_pathway">https://github.com/haolu123/GPNet_pathway

Accurate diagnosis of middle ear diseases using otoscopy remains challenging, particularly in primary care settings where clinician experience with otoscopy varies and visual examination alone may not fully capture middle ear physiology. Although AI-based models have shown promise for automated otoscopy interpretation, most rely primarily on visual information, creating an opportunity to improve diagnostic robustness by integrating complementary physiological measurements. Here, we present OtoTymp-AI, a confidence-guided multimodal decision-fusion framework that integrates otoscopy video analysis with conventional 226 Hz tympanometry. A convolutional neural network was trained to classify otoscopy videos, and tympanometric measurements were incorporated through a clinically interpretable, rule-guided decision strategy to refine visually uncertain predictions rather than through jointly trained multimodal representation learning. Within this multi-center otoscopy study, multimodal evaluation was performed in one independent external cohort comprising 104 videos with paired tympanometry across six diagnostic categories. In this exploratory paired-cohort analysis, the video-only CNN achieved an overall accuracy of 63.46% (95% CI, 53.88–72.08), while the hybrid video-plus-tympanometry framework achieved 81.73% accuracy (95% CI, 73.22–87.98) at the best observed threshold of 0.90, corresponding to an observed absolute improvement of 18.27 percentage points over the CNN-only model (95% paired bootstrap CI, 9.62–26.92). Improvements were observed for selected categories represented in this cohort, including effusion and retraction, although confidence intervals were wide for categories with small positive sample sizes. These findings suggest that integrating anatomical video information with physiological tympanometry may improve AI-assisted middle ear diagnosis in this external paired cohort, but the operating threshold and per-class performance require confirmation in larger prospective paired multimodal cohorts.