Development of an AI based Web Application for Precision Medicine in Human lung cancer
The web application will employ a deep learning-based model to analyze histopathological images of lung tissue and classify tumor subtypes with high accuracy. In addition, predictive ML models will be trained on patient-specific genomic and clinical datasets, providing personalized treatment recommendations and survival predictions. Advanced natural language processing (NLP) modules will be integrated to facilitate real-time literature mining, ensuring that clinicians stay updated with the latest therapeutic strategies, biomarker discoveries, and clinical trial results.
This AI-driven web application will significantly enhance the ability to make data-driven decisions in lung cancer care. Further innovations include reinforcement learning models for adaptive decision-making, along with federated learning frameworks that enable collaborative research while preserving data privacy. By leveraging the power of AI technologies in precision medicine, this project has the potential to transform lung cancer management and improve patient outcomes globally.
Objectives of the Lung Cancer Detection and Treatment Project:
This project aims to develop AI-based solutions to improve lung cancer diagnosis, survival rates, and personalized treatment for patients. The following are the main objectives of the project:
Early Detection of Lung Cancer
The goal is to develop a model capable of detecting lung cancer from H&E-stained histopathological images. This involves analyzing tissue samples to identify cancerous cells and classifying them by cancer type. The model should be able to detect cancer early and classify it accurately.
Survival Prediction for Patients
This model aims to predict how long a patient will survive after a lung cancer diagnosis based on clinical factors such as age, cancer stage, genetic mutations, and treatments followed. The goal is to provide accurate survival predictions, helping to make better decisions regarding patient care.
Personalized Treatment Recommendations
The objective is to provide personalized treatment recommendations based on the patient’s medical profile, including their age, comorbidities, cancer stage, and genetic mutations. The system should suggest the most effective treatments based on these individual factors, improving treatment outcomes.
Extraction of Recent Medical Discoveries
This model will extract and summarize the latest scientific discoveries related to lung cancer from medical articles and scientific publications. It will help healthcare professionals stay updated on the latest treatments, biomarkers, and clinical trials, enabling them to apply the newest knowledge in their practice.
Federated Learning for Data Privacy
One of the important goals of this project is to use Federated Learning to train models on medical data from different hospitals without transferring sensitive patient data between institutions. This ensures patient privacy while improving model performance.
Expected Results:
Early Detection Model: A system capable of detecting and classifying lung cancer from histopathological images.
Survival Prediction Model: A model that predicts a patient's survival duration based on clinical data.
Personalized Treatment Recommendation System: A tool that offers personalized treatment suggestions based on the patient’s medical profile.
Automated Medical Discovery Extraction Tool: A system that summarizes recent medical research on lung cancer.
Federated Learning Model: A model that ensures data privacy while improving performance through collaborative learning across hospitals.
The project aims to improve early diagnosis, personalize treatments, and provide quick access to the latest scientific discoveries, while ensuring the protection of patient data.
Collaborative Proposals for Masters in Bioinformatics and Data Science Students, ENSA, Abdelmalek Essaadi University, Tangier Morocco and CEHTI, JUIT, Solan, India