A study finds image analysis using machine learning can identify haematological malignancies.

Image analysis is typically used to extract meaningful information from images. It can perform tasks like finding shapes, identifying edges, removing noise, counting objects, etc. for image quality. Recently, a study demonstrated that image analysis utilizing neural networks can help detect details in tissue samples that are difficult to determine with the human eye. Myelodysplastic syndrome (MDS) is a disease of the stem cells in the bone marrow, which affects the maturation and differentiation of blood cells. Diagnosing MDS requires a bone marrow sample to investigate genetic changes in the bone marrow cells.

Annually, some 200 Finns are diagnosed with MDS, which can develop into acute leukaemia. The incidence of MDS globally is 4 cases per 100,000 person years. The syndrome is classified into groups to find out the nature of the disorder in more detail.

In the University of Helsinki study, microscopic images of patients’ bone marrow samples suffering from myelodysplastic syndrome were analysed utilising an image analysis technique based on machine learning. The samples were stained with haematoxylin and eosin (H&E staining), a procedure of routine diagnostics for the disease. The slides were digitised and analysed using computational deep learning models.

The study was published in the Blood Cancer Discovery, a journal of the American Association for Cancer Research. The results can be explored with an interactive tool: http://hruh-20.it.helsinki.fi/mds_visualization/.

With machine learning, the digital image dataset could be assessed to accurately identify the most common genetic mutations affecting the progression of the syndrome, such as acquired mutations and chromosomal aberrations. The higher the number of abnormal cells in the samples, the higher the reliability of the results generated by the prognostic models.

The study uses the data analysis technique to support the diagnosis. One of the greatest challenges of leveraging neural network models is to understand the criteria on which they base their conclusions drawn from data, such as information contained in images. The University of Helsinki study succeeded in determining what deep learning models see in tissue samples when they have been taught to look for, for example, genetic mutations related to MDS. The technique provides new information on the effects of complex diseases on bone marrow cells and the surrounding tissues.

According to Professor Satu Mustjoki, ‘the study confirms that computational analysis helps to identify features that elude the human eye. Moreover, data analysis helps to collect quantitative data on cellular changes and their relevance to the patient’s prognosis.’

Part of the analytics carried out in the study was implemented using the Helsinki University Hospital (HUS) data lake environment, which enables the efficient collection and analysis of extensive clinical datasets.

“We’ve developed solutions to structure and analyse data stored in the HUS data lake. Image analysis helps us analyse large quantities of biopsies and rapidly produce diverse information on disease progression. The techniques developed in the project are suited to other projects as well, and they are perfect examples of digitalizing medical science,” says doctoral student Oscar Bruck.

Ph.D. Olivier Elemento from the Caryl and Israel Englander Institute for Precision Medicine says, “[This] study provides new insights into the pathobiology of MDS and paves the way for increased use of artificial intelligence for the assessment and diagnosis of hematological malignancies.”

The post IMAGE ANALYSIS USING ML IDENTIFIES HAEMATOLOGICAL MALIGNANCIES appeared first on Artificial Intelligence.

Technology research group Gartner has identified its top 10 data and analytics (D&A) technology trends to help organisations prepare for a post-pandemic reset.

According to Rita Sallam, distinguished research vice president at Gartner, “To innovate their way beyond COVID-19, data and analytics leaders require an ever-increasing speed and scale of analysis in terms of both processing and access to succeed.”

The list includes;

• Smarter, Faster, More Responsible AI: By the end of 2024, 75% of organisations will shift from piloting to operationalizing artificial intelligence (AI), driving a 5 times increase in streaming data and analytics infrastructures.Within the current pandemic context, AI techniques such as machine learning (ML), optimisation and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures.Other smarterAI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems that model and stimulate complex systems.

• Decline of the Dashboard: Dynamic data stories with more automated and consumerised experiences will replace visual, point-and-click authoring and exploration. As a result, the amount of time users spend using predefined dashboards will decline. The shift to dynamic data stories that leverage for example augmented analytics or NLP, means that the most relevant insights will stream to each user based on their context, role or use.

• Decision Intelligence: By 2023, more than 33% of large organisations will have analysts practicing decision intelligence, including decision modelling. Decision intelligence brings together several disciplines, including decision management and decision support. It provides a framework to help data and analytics leaders design, model, align, execute, monitor and tune decision models and processes in the context of business outcomes and behaviour.

• X Analytics: Gartner coined the term “X analytics” to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.During the COVID-19 pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. X analytics combined with AI and other techniques such as graph analytics will play a key role in identifying, predicting and planning for natural disasters and other crises in the future.

• Augmented Data Management: Augmented data management uses ML and AI techniques to optimise and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powering dynamic systems.Augmented data management products can examine large samples of operational data, including actual queries, performance data and schemas. Using the existing usage and workload data, an augmented engine can tune operations and optimise configuration, security and performance.

• Cloud is a Given: By 2022, public cloud services will be essential for 90% of data and analytics innovation. As data and analytics moves to the cloud, data and analytics leaders still struggle to align the right services to the right use cases, which leads to unnecessary increased governance and integration overhead.The question for data and analytics is moving from how much a given service costs to how it can meet the workload’s performance requirements beyond the list price. Data and analytics leaders need to prioritise workloads that can exploit cloud capabilities and focus on cost optimisation when moving to cloud.

• Data and Analytics Worlds Collide: Data and analytics capabilities have traditionally been considered distinct entities and managed accordingly. Vendors offering end-to-end workflows enabled by augmented analytics blur the distinction between the two markets.The collision of data and analytics will increase interaction and collaboration between historically separate data and analytics roles. This impacts not only the technologies and capabilities provided, but also the people and processes that support and use them. The spectrum of roles will extend from traditional data and analytics to information explorer and citizen developer as examples.

• Data Marketplaces and Exchanges: By 2022, 35% of large organisations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020. Data marketplaces and exchanges provide single platforms to consolidate third-party data offerings and reduce costs for third-party data.

• Blockchain in Data and Analytics: Blockchain technologies address two challenges in data and analytics. First, blockchain provides the full lineage of assets and transactions. Second, blockchain provides transparency for complex networks of participants.Outside of limited bitcoin and smart contract use cases, ledger database management systems (DBMSs) will provide a more attractive option for single-enterprise auditing of data sources. By 2021, Gartner estimates that most permissioned blockchain uses will be replaced by ledger DBMS products.

• Relationships Form the Foundation of Data and Analytics Value: By 2023, graph technologies will facilitate rapid contextualisation for decision making in 30% of organisations worldwide. Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organisations, people and transactions. It helps data and analytics leaders find unknown relationships in data and review data not easily analysed with traditional analytics.

The post Gartner Identifies Top 10 Data And Analytics Technology Trends For 2020 appeared first on Artificial Intelligence.

Machine learning is helping Penn Medicine researchers identify the size and shape of brain networks in individual children, which may be useful for understanding psychiatric disorders. In a new study published today in the journal Neuron, a multidisciplinary team showed how brain networks unique to each child can predict cognition. The study—which used machine learning techniques to analyze the functional magnetic resonance imaging (fMRI) scans of nearly 700 children, adolescents, and young adults—is the first to show that functional neuroanatomy can vary greatly among kids, and is refined during development.

The human brain has a pattern of folds and ridges on its surface that provide physical landmarks for finding brain areas. The functional networks that govern cognition have long been studied in humans by lining up activation patterns—the software of the brain—to the hardware of these physical landmarks. However, this process assumes that the functions of the brain are located on the same landmarks in each person. This works well for many simple brain systems, for example, the motor system controlling movement is usually right next to the same specific fold in each person. However, multiple recent studies in adults have shown this is not the case for more complex brain systems responsible for executive function—a set of mental processes which includes self-control and attention. In these systems, the functional networks do not always line up with the brain’s physical landmarks of folds and ridges. Instead, each adult has their own specific layout. Until now, it was unknown how such person-specific networks might change as kids grow up, or relate to executive function.

“The exciting part of this work is that we are now able to identify the spatial layout of these functional networks in individual kids, rather than looking at everyone using the same ‘one size fits all’ approach,” said senior author Theodore D. Satterthwaite, MD, an assistant professor of Psychiatry in the Perelman School of Medicine at the University of Pennsylvania. “Like adults, we found that functional neuroanatomy varies quite a lot among different kids—each child has a unique pattern. Also like adults, the networks that vary the most between kids are the same executive networks responsible for regulating the sorts of behaviors that can often land adolescents in hot water, like risk taking and impulsivity.”

To study how functional networks develop in children and supports executive function, the team analyzed a large sample of adolescents and young adults (693 participants, ages 8 to 23). These participants completed 27 minutes of fMRI scanning as part of the Philadelphia Neurodevelopmental Cohort (PNC) a large study that was funded by the National Institute of Mental Health. Machine learning techniques developed by the laboratory of Yong Fan, PhD, an assistant professor of Radiology at Penn and co-author on the paper, allowed the team to map 17 functional networks in individual children, rather than relying on the average location of these networks.

The researchers then examined how these functional networks evolved over adolescence, and were related to performance on a battery of cognitive tests. The team found that the functional neuroanatomy of these networks was refined with age, and allowed the researchers to predict how old a child with a high degree of accuracy.

“The spatial layout of these networks predicted how good kids were at executive tasks,” said Zaixu Cui, PhD, a post-doctoral fellow in Satterthwaite’s lab and the paper’s first author. “Kids who have more ‘real estate’ on their cortex devoted to networks responsible for executive function in fact performed better on these complex tasks.” In contrast, youth with lower executive function had less of their cortex devoted to these executive networks.

Taken together, these results offer a new account of developmental plasticity and diversity and highlight the potential for progress in personalized diagnostics and therapeutics, the authors said.

“The findings lead us to interesting questions regarding the developmental biology of how these networks are formed, and also offer potential for personalizing neuromodulatory treatments, such as brain stimulation for depression or attention problems,” said Satterthwaite. “How are these systems laid down in the first place? Can we get a better response for our patients if we use neuromodulation that is targeted using their own personal networks? Focusing on the unique features of each person’s brain may provide an imporant way forward.”

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

The post Artificial Intelligence (AI) Identifies Personalized Brain Networks in Children appeared first on Artificial Intelligence.