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Purpose: Many neurosurgical planning tasks rely on identifying points of interest in volumetric images. Often, these points require significant expertise to identify correctly as, in some cases, they are not visible but instead inferred by the clinician. This leads to a high degree of variability between annotators selecting these points. In particular, errors-of-type are when the experts fundamentally select different points rather than the same point with some inaccuracy. This complicates research as their mean may not reflect any of the experts’ intentions nor the ground truth. Methods: We present a regularised Bayesian model for measuring errors-of-type in pointing tasks. This model is reference-free, in that it doesn’t require a priori knowledge of the ground truth point but instead works on the basis of the level of consensus between multiple annotators. We apply this model to simulated data and clinical data from transcranial magnetic stimulation for chronic pain. Results: Our model estimates the probabilities of selecting the correct point in the range of 82.6-88.6% with uncertainties in the range of 2.8-4.0%. This agrees with the literature where ground truth points are known. The uncertainty has not previously been explored in the literature, and gives an indication of the dataset’s strength. Conclusions: Our reference-free Bayesian framework easily models errors-of-type in pointing tasks. It allows for clinical studies to be performed with a limited number of annotators where the ground truth is not immediately known, which can be applied widely for better understanding human errors in neurosurgical planning.

International audience; Medical information processing is a staple of modern medicine with its increasing focus on the collection of numeric medical data such as questionnaires, biophysiological signals, and medical images. Although these modalities have long existed and guided medical practice, the movement towards using algorithms to transform, curate, summarise, and otherwise interact with this data is relatively new. Novel algorithms now form the interface between clinical users and data, extracting information that would otherwise be inaccessible or cumbersome. Recently, machine learning has expanded the capacities of these algorithms, using a priori acquired (and often annotated) datasets to learn a complex computational task. Validation of these techniques is inherently important for determining their safety and efficacy in a particular clinical context. However, methodological considerations such as the definition of reference data and validation procedures can obscure validation issues such as inaccurate reporting, a lack of standardisation, and a variety of biases. The purpose of this paper is to develop a framework for understanding medical information processing algorithms with a focus on validation that is adapted for machine learning approaches as well as traditional ones. This framework is instantiated in two example literature reviews which serve as the starting point for a discussion on how validation can be improved cognisant of machine learning. © 2023

International audience

International audience; Machine learning is undoubtedly becoming more and more integrated into medical image computing research and practice. As with any conceptual or technological tool, this exposure requires the community to better understand the logical and philosophical foundations underpinning its use. One of the these (largely unexamined) areas is the learning paradigm that motivates everything from problem decomposition to architecture selection to clinical regulation. This article examines one of the spectra that defines the learning paradigm, specifically the spectrum between end-to-end and separable learning, in order to analyse what values drive solutions towards or away from either extreme. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

International audience; Disinhibition is a core symptom of many neurodegenerative diseases, particularly frontotemporal dementia, and is a major cause of stress for caregivers. While a distinction between behavioural and cognitive disinhibition is common, an operational definition of behavioural disinhibition is still missing. Furthermore, conventional assessment of behavioural disinhibition, based on questionnaires completed by the caregivers, often lacks ecological validity. Therefore, their neuroanatomical correlates are non-univocal. In the present work, we used an original behavioural approach in a semi-ecological situation to assess two specific dimensions of behavioural disinhibition: compulsivity and social disinhibition. First, we investigated disinhibition profile in patients compared to controls. Then, to validate our approach, compulsivity and social disinhibition scores were correlated with classic cognitive tests measuring disinhibition (Hayling Test) and social cognition (mini-Social cognition and Emotional Assessment). Finally, we disentangled the anatomical networks underlying these two subtypes of behavioural disinhibition, taking in account the grey (voxel-based morphometry) and white matter (diffusion tensor imaging tractography). We included 17 behavioural variant frontotemporal dementia patients and 18 healthy controls. We identified patients as more compulsive and socially disinhibited than controls. We found that behavioural metrics in the semi-ecological task were related to cognitive performance: compulsivity correlated with the Hayling test and both compulsivity and social disinhibition were associated with the emotion recognition test. Based on voxel-based morphometry and tractography, compulsivity correlated with atrophy in the bilateral orbitofrontal cortex, the right temporal region and subcortical structures, as well as with alterations of the bilateral cingulum and uncinate fasciculus, the right inferior longitudinal fasciculus and the right arcuate fasciculus. Thus, the network of regions related to compulsivity matched the "semantic appraisal" network. Social disinhibition was associated with bilateral frontal atrophy and impairments in the forceps minor, the bilateral cingulum and the left uncinate fasciculus, regions corresponding to the frontal component of the "salience" network. Summarizing, this study validates our semi-ecological approach, through the identification of two subtypes of behavioural disinhibition, and highlights different neural networks underlying compulsivity and social disinhibition. Taken together, these findings are promising for clinical practice by providing a better characterisation of inhibition disorders, promoting their detection and consequently a more adapted management of patients.