Machine learning enhanced virtual autopsy

摘要

STATE-OF-THE-ART AND OBJECTIVES: In many cases, access to biological samples is needed for molecular analyses. Biopsies or other minimally-invasive sampling techniques, in the context of virtual autopsy (virtopsy), would reduce risk and improve societal acceptance when compared with traditional autopsies. We propose a study that compares and contrasts virtopsy with traditional autopsy, using large study groups based on four different levels of information: Macroscopic; Microscopic; Medical imaging; and Molecular phenotypes. This proposal includes the development of a machine learning enhanced virtual biobank to understand the process of diseases, and to evaluate clinical diagnosis and treatment. This virtual biobank, enhanced by machine learning and knowledge extraction approaches, will be composed of a unique collection of non-invasively generated autopsy images (e.g., X-ray, computerized tomography, magnetic resonance imaging, ultrasound) and digital pathology imaging data of corresponding biological samples. Based on this vast resource, we will experimentally design, develop, test and evaluate machine learning algorithms that can self-learn from, as well as make predictions (based on the virtual biobank data). These ambitious studies will help us go beyond the state-of-the-art to demonstrate to what extent machine learning can enhance medical expertise, so as to understand the process of diseases and to evaluate both clinical diagnosis and treatment. The algorithms will make data-driven predictions or decisions, by building a model from sample inputs; a variety of subject data was used. We will use the same machine learning approach developed by a research group at the Medical University of Graz. 1 They are world-leading experts in this field with long-standing experience in the application domain, health generally, pathology and metabolomics 2 in particular. This strategy will allow achievement of results that include the use of fewer images when compared with conventional machine learning (e.g., deep learning approaches), and so helps to alleviate some major issues one would expect for this type of study set-up. In the application of virtopsy we do not have access to many cases, (i.e., sample-size is small, and not the large numbers in the thousands to millions which are usually necessary), and so consequently we need algorithms which are able to self-learn (with only a few examples), in the same way humans do. The chosen approach of interactive machine learning is an inventive way to overcome this problem. The intention is to utilize this method of smart injective machine learning. It is not a black box method, which demands the input of a massive amount of data. Instead, expert knowledge is fed directly into the algorithmic loop. 1 This is an ingenious method to deal with standard limitations. The necessity of large amounts of data/sample-size and cases will always be a limitation for conventional machine learning. Our technique will have a unique resource to drive the project and achieve our anticipated results. Regarding virtual microscopy, digital pathology now provides an entirely innovative technique to analyze image data from tissues. We propose a study that conducts a systematic comparison between the morphology of tissue (as seen by digital pathology) and medical images such as magnetic resonance imaging (MRI). In addition, pathologists will provide images using a medical care standard digital camera for macroscopic documentation and analysis. This image will be compared with corresponding medical images, as different levels of imaging information add to the originality of this study. Finally, we propose to include nuclear mass resonance (NMR) spectroscopy in the active research of this study. This adds to the originality and scientific aspect as it involves a new level of information regarding metabolic status and morphology of tissue. Consequently, focus is not only on explaining disease biology, but it is also developing machine learning algorithms that provide image analysis – currently there is a lack of literature on large-scale programmes that compare MRI images with both NMR and microscopic images. NMR will enable us to obtain the metabolomic profile, as the metabolome provides detailed information about the chemical composition of the tissue. For NMR metabolomics, it is essentially the same principle as for MRI, but the read-out of the signal is different. This is scientifically challenging but relevant, as we can then compare MRI data with chemical composition of tissue investigated. In addition, we will compare this data with histological and macroscopic data. There has been no current literature to date demonstrating that this has been undertaken in such a coordinated approach as set out in our proposal. Ideally, for this type of image analysis study, we intend to include many additional datasets from autopsies performed rapidly (~3 hours) after clinical death. This is important