Mathrusri Ramabai Ambedkar Dental College & Hospital

Using ADNI data, this study builds a patient-level multimodal fusion framework based on ResNet50 transfer learning that concatenates MRI and PET imaging features with clinical variables (ADAS11, ADAS13, APOE4, age, sex, education, MMSE total) in a fully connected network to produce three-way AD/MCI/CN predictions, with Grad-CAM heatmaps for explanation; the ablation shows accuracy rising as modalities are added, from 39.61% for MRI alone and 51.72% for PET alone to 66.67% for clinical-only, 54.17% for MRI+PET, and 79.17% for the full three-modality model on a 24-patient test cohort (95% Wilson interval roughly 59.5%-90.8%), with the addition of clinical data producing the single largest gain.

Using ADNI data, this study builds a patient-level multimodal fusion framework based on ResNet50 transfer learning that concatenates MRI and PET imaging features with clinical variables (ADAS11, ADAS13, APOE4, age, sex, education, MMSE total) in a fully connected network to produce three-way AD/MCI/CN predictions, with Grad-CAM heatmaps for explanation; the ablation shows accuracy rising as modalities are added, from 39.61% for MRI alone and 51.72% for PET alone to 66.67% for clinical-only, 54.17% for MRI+PET, and 79.17% for the full three-modality model on a 24-patient test cohort (95% Wilson interval roughly 59.5%-90.8%), with the addition of clinical data producing the single largest gain.

This paper presents Universal Doc AI, a multimodal intelligent document management system that unifies Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), vector-based semantic search, and Large Language Models (LLMs) into a single platform that extracts text from user-uploaded PDFs, scans, images, and handwritten notes, chunks and embeds it for vector indexing, and answers natural-language questions grounded in the uploaded documents, reporting improvements in retrieval accuracy, response relevance, and interaction efficiency over traditional keyword-based search.

This paper presents Universal Doc AI, a multimodal intelligent document management system that unifies Optical Character Recognition (OCR), Retrieval-Augmented Generation (RAG), vector-based semantic search, and Large Language Models (LLMs) into a single platform that extracts text from user-uploaded PDFs, scans, images, and handwritten notes, chunks and embeds it for vector indexing, and answers natural-language questions grounded in the uploaded documents, reporting improvements in retrieval accuracy, response relevance, and interaction efficiency over traditional keyword-based search.