Medical open network for AI

Medical open network for AI (MONAI) is an open-source, community-supported framework for Deep learning (DL) in healthcare imaging. MONAI provides a collection of domain-optimized implementations of various DL algorithms and utilities specifically designed for medical imaging tasks. MONAI is used in research and industry, aiding the development of various medical imaging applications, including image segmentation, image classification, image registration, and image generation.
MONAI was first introduced in 2019 by a collaborative effort of engineers from Nvidia, the National Institutes of Health, and the King's College London academic community. The framework was developed to address the specific challenges and requirements of DL applied to medical imaging.
Built on top of PyTorch, a popular DL library, MONAI offers a high-level interface for performing everyday medical imaging tasks, including image preprocessing, augmentation, DL model training, evaluation, and inference for diverse medical imaging applications. MONAI simplifies the development of DL models for medical image analysis by providing a range of pre-built components and modules.
MONAI is part of a larger suite of Artificial Intelligence (AI)-powered software called NVIDIA Clara. Besides MONAI, Clara also comprises NVIDIA Parabricks for genome analysis.
Medical imaging is a range of imaging techniques and technologies that enables clinicians to visualize the internal structures of the human body. It aids in diagnosing, treating, and monitoring various medical conditions, thus allowing healthcare professionals to obtain detailed and non-invasive images of organs, tissues, and physiological processes.
Medical imaging has evolved, driven by technological advancements and scientific understanding. Today, it encompasses modalities such as X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), ultrasound, nuclear medicine, and digital pathology, each offering capabilities and insights into human anatomy and pathology.
The images produced by these medical imaging modalities are interpreted by radiologists, trained specialists in analyzing and diagnosing medical conditions based on the visual information captured in the images. In recent years, the field has witnessed advancements in computer-aided diagnosis, integrating Artificial intelligence and Deep learning techniques to automatize medical image analysis and assist radiologists in detecting abnormalities and improving diagnostic accuracy.
MONAI provides a robust suite of libraries, tools, and Software Development Kits (SDKs) that encompass the entire process of building medical imaging applications. It offers a comprehensive range of resources to support every stage of developing Artificial intelligence (AI) solutions in the field of medical imaging, from initial annotation (MONAI Label), through models development and evaluation (MONAI Core), and final application deployment (MONAI deploy application SDK).
MONAI Label is a versatile tool that enhances the image labeling and learning process by incorporating AI assistance. It simplifies the task of annotating new datasets by leveraging AI algorithms and user interactions. Through this collaboration, MONAI Label trains an AI model for a specific task and continually improves its performance as it receives additional annotated images. The tool offers a range of features and integrations that streamline the annotation workflow and ensure seamless integration with existing medical imaging platforms.
Within MONAI Core, researchers can find a collection of tools and functionalities for dataset processing, loading, Deep learning (DL) model implementation, and evaluation. These utilities allow researchers to evaluate the performance of their models. MONAI Core offers customizable training pipelines, enabling users to construct and train models that support various learning approaches such as supervised, semi-supervised, and self-supervised learning. Additionally, users have the flexibility to implement different computing strategies to optimize the training process.
The MONAI deploy application SDK offers a systematic series of steps empowering users to develop and fine-tune their AI models and workflows for deployment in clinical settings. These steps act as checkpoints, guaranteeing that the AI inference infrastructure adheres to the essential standards and requirements for seamless clinical integration.
Key components of the MONAI Deploy Application SDK include:
MONAI has found applications in various research studies and industry implementations across different anatomical regions. For instance, it has been utilized in academic research involving automatic cranio-facial implant design, brain tumor analysis from Magnetic Resonance images, identification of features in focal liver lesions from MRI scans, radiotherapy planning for prostate cancer, preparation of datasets for fluorescence microscopy imaging, and classification of pulmonary nodules in lung cancer.
In healthcare settings, hospitals have leveraged MONAI to enhance mammography reading by employing Deep learning models for breast density analysis. This approach reduce the waiting time for patients, allowing them to receive mammography results within 15 minutes. Consequently, clinicians save time, and patients experience shorter wait times. This advancement enables patients to engage in immediate discussions with their clinicians during the same appointment, facilitating prompt decision-making and discussion of next steps before leaving the facility. Moreover, hospitals can employ MONAI to identify indications of a COVID-19 patient's deteriorating condition or determine if they can be safely discharged, optimizing patient care and post-COVID-19 decision-making.
In the corporate realm, companies choose MONAI to develop product applications addressing various clinical challenges. These include ultrasound-based scoliosis assessment, Artificial intelligence-based pathology image labeling, in-field pneumothorax detection using ultrasound, characterization of brain morphology, detection of micro-fractures in teeth, and non-invasive estimation of intracranial pressure.