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Autism Spectrum Disorder (ASD) is a pervasive disorder that affects roughly one in a hundred people worldwide. Currently, the diagnosis is based on clinical observation. In practice, the quite drastic diagnosis of ASD may be issued after only a 15-minute conversation. The limits of observation-based diagnosis have been addressed and therefore the paradigm is called to shift toward an objective diagnosis based on a measurement. Many studies have been published in which methodologies are described to classify individuals with ASD from healthy controls based on data obtained from magnetic resonance imaging (MRI). The question is if these studies contain overlap which can be used to describe the state of the art of an objective diagnosis of ASD using MRI. The potential biases and limitations of the state of the art are also considered.

Available online 8 June 2021. A variety of strategies are used to combine multi-echo functional magnetic resonance imaging (fMRI) data, yet recent literature lacks a systematic comparison of the available options. Here we compare six different approaches derived from multi-echo data and evaluate their influences on BOLD sensitivity for offline and in particular real- time use cases: a single-echo time series (based on Echo 2), the real-time T 2 ∗ -mapped time series ( T 2 ∗ FIT ) and four combined time series ( T 2 ∗ -weighted, tSNR-weighted, TE-weighted, and a new combination scheme termed T 2 ∗ FIT -weighted). We compare the influences of these six multi-echo derived time series on BOLD sensitivity using a healthy participant dataset ( N = 28) with four task-based fMRI runs and two resting state runs. We show that the T 2 ∗ FIT -weighted combination yields the largest increase in temporal signal-to-noise ratio across task and resting state runs. We demonstrate additionally for all tasks that the T 2 ∗ FIT time series consistently yields the largest offline effect size measures and real-time region-of-interest based functional contrasts and temporal contrast-to- noise ratios. These improvements show the promising utility of multi-echo fMRI for studies employing real-time paradigms, while further work is advised to mitigate the decreased tSNR of the T 2 ∗ FIT time series. We recommend the use and continued exploration of T 2 ∗ FIT for offline task-based and real-time region-based fMRI analysis. Supporting information includes: a data repository ( https://dataverse.nl/dataverse/rt-me-fmri ), an interactive web-based application to explore the data ( https://rt-me-fmri.herokuapp.com/ ), and further materials and code for reproducibility ( https://github.com/jsheunis/rt- me- fMRI ). This work was funded by the foundation Health-Holland LSH-TKI (grant LSHM16053-SGF ) and supported by Philips Research. LH was supported by the European Union’s Horizon 2020 research and inno- vation program under the Grant Agreement no 794395 . CCG was sup- ported by the Spanish Ministry of Economy and Competitiveness (Ra- mon y Cajal Fellowship, RYC-2017- 21845 ), the Basque Government ( PIBA_2019_104 ) and the Spanish Ministry of Science, Innovation and Universities ( MICINN ; PID2019-105520GB-100 ).

Routine surveillance endoscopies are currently used to detect dysplasia in patient with Barrett's Esophagus (BE). However, most of these procedures are performed by non-expert endoscopists in community hospitals. Leading to many missed dysplastic lesions, which can progress into advanced esophageal adenocarcinoma if left untreated.1 In recent years, several successful algorithms have been proposed for the detection of cancer in BE using high-quality overview images. This work addresses the first steps towards clinical application on endoscopic surveillance videos. Several challenges are identified that occur when moving from image-based to video-based analysis. (1) It is shown that algorithms trained on high-quality overview images do not naively transfer to endoscopic videos due to e.g. non-informative frames. (2) Video quality is shown to be an important factor in algorithm performance. Specifically, temporal location performance is highly correlated with video quality. (3) When moving to real-time algorithms, the additional compute necessary to address the challenges in videos will become a burden on the computational budget. However, in addition to challenges, videos also bring new opportunities not available in the current image-based methods such as the inclusion of temporal information. This work shows that a multi-frame approach increases performance compared to a naive single-image method when the above challenges are addressed.