Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine’s multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine’s “big data”, in the context of genetics, genomics, and beyond.

In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.

BACKGROUND AND PURPOSE: Pre-eclampsia is a serious clinical gestational disorder occurring in 3%–5% of all human pregnancies and characterized by endothelial dysfunction and vascular complications. Offspring born of pre-eclamptic pregnancies are reported to exhibit deficits in cognitive function, higher incidence of depression, and increased susceptibility to stroke. However, no brain imaging reports exist on these offspring. We aimed to assess brain structural and vascular anatomy in 7- to 10-year-old offspring of pre-eclamptic pregnancies compared with matched controls. MATERIALS AND METHODS: Offspring of pre-eclamptic pregnancies and matched controls (n = 10 per group) were recruited from an established longitudinal cohort examining the effects of pre-eclampsia. Children underwent MR imaging to identify brain structural and vascular anatomic differences. Maternal plasma samples collected at birth were assayed for angiogenic factors by enzyme-linked immunosorbent assay. RESULTS: Offspring of pre-eclamptic pregnancies exhibited enlarged brain regional volumes of the cerebellum, temporal lobe, brain stem, and right and left amygdalae. These offspring displayed reduced cerebral vessel radii in the occipital and parietal lobes. Enzyme-linked immunosorbent assay analysis revealed underexpression of the placental growth factor among the maternal plasma samples from women who experienced pre-eclampsia. CONCLUSIONS: This study is the first to report brain structural and vascular anatomic alterations in the population of offspring of pre-eclamptic pregnancies. Brain structural alterations shared similarities with those seen in autism. Vascular alterations may have preceded these structural alterations. This pilot study requires further validation with a larger population to provide stronger estimates of brain structural and vascular outcomes among the offspring of pre-eclamptic pregnancies.

Magnetic resonance angiography (MRA) can capture the variation of cerebral arteries with high spatial resolution. These measurements include valuable information about the morphology, geometry, and density of brain arteries, which may be useful to identify risk factors for cerebrovascular and neurological diseases at an early time point. However, this requires knowledge about the distribution and morphology of vessels in healthy subjects. The statistical arterial brain atlas described in this work is a free and public neuroimaging resource that can be used to identify vascular morphological changes. The atlas was generated based on 544 freely available multi-center MRA and T1-weighted MRI datasets. The arteries were automatically segmented in each MRA dataset and used for vessel radius quantification. The binary segmentation and vessel size information were non-linearly registered to the MNI brain atlas using the T1-weighted MRI datasets to construct atlases of artery occurrence probability, mean artery radius, and artery radius standard deviation. This public neuroimaging resource improves the understanding of the distribution and size of arteries in the healthy human brain.

Objectives: Agitation and aggression are common in dementia and pre‐dementia. The dementia risk syndrome mild behavioral impairment (MBI) includes these symptoms in the impulse dyscontrol domain. However, the neural circuitry associated with impulse dyscontrol in neurodegenerative disease is not well understood. The objective of this work was to investigate if regional micro‐ and macro‐structural brain properties were associated with impulse dyscontrol symptoms in older adults with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD).Methods: Clinical, neuropsychiatric, and T1‐weighted and diffusion‐tensor magnetic resonance imaging (DTI) data from 80 individuals with and 123 individuals without impulse dyscontrol were obtained from the AD Neuroimaging Initiative. Linear mixed effect models were used to assess if impulse dyscontrol was related to regional DTI and volumetric parameters.Results: Impulse dyscontrol was present in 17% of participants with NC, 43% with MCI, and 66% with AD. Impulse dyscontrol was associated with: (1) lower fractional anisotropy (FA), and greater mean, axial, and radial diffusivity in the fornix; (2) lesser FA and greater radial diffusivity in the superior fronto‐occipital fasciculus; (3) greater axial diffusivity in the cingulum; (4) greater axial and radial diffusivity in the uncinate fasciculus; (5) gray matter atrophy, specifically, lower cortical thickness in the parahippocampal gyrus.Conclusion: Our findings provide evidence that well‐established atrophy patterns of AD are prominent in the presence of impulse dyscontrol, even when disease status is controlled for, and possibly in advance of dementia. Our findings support the growing evidence for impulse dyscontrol symptoms as an early manifestation of AD.