Deep Learning in Preventive Healthcare

Deep Learning in Preventive Healthcare

Deep Learning in Preventive Healthcare The healthcare artificial intelligence segment is projected to grow 40 percent annually between now until 2024. Deep Learning in Preventive healthcare would become the fastest growing component of this huge demand in Healthcare AI. AI’s deep learning algorithms are designed to detect features in huge, disparate datasets that are not discernible to entire teams of data scientists. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics. Artificial Neural Networks are a network of interconnected nodes that make up a model.

With the help from expert Deep Learning in Preventive Healthcare team of AltaFuituris, we help our clients to bridge the gap between last-generation DL techniques and biomedical and clinical disciplines—such as cellular and molecular biology, pathology, oncology, and neurology—to both generate novel, multimodal, and multidomain biomarkers and augment the potential for innovative, personalized patient-management strategies.

Few areas (included but are not limited) where Alta-Futuris team can provide solutions to our clients in Healthcare and Life Sciences:


  • Strategies for interpretation and visualization of DNN models
  • AI-based reconstruction, processing, and analysis of medical images
  • Deep learning-based analysis of histological/cytological images
  • Predicting histological/cytological tissue characteristics from non-invasive in vivo imaging (MRI, PET, and CT)
  • Multimodal and multidomain medical data integration
  • Strategies for handling data sparsity and paucity in medical applications of DL
  • Intelligent vital signs monitoring in intensive care units
  • Intelligent patient stratification and scheduling in diagnostic facilities
  • Artificial-intelligence-based therapeutic decision support
  • AI strategies for prediction of longitudinal patient trajectories
  • Deep learning in preclinical imaging
  • Deep-radiomics: image segmentation and analysis
  • Deep learning for genome-scale-omics and multiomics data analysis
  • Automated reasoning and metareasoning in medicine
  • Models and systems for network-based analysis of general population health