What brain abnormalities can magnetic resonance imaging detect in foetal and early neonatal spina bifida: a systematic review

PURPOSE: Open spina bifida (OSB) encompasses a wide spectrum of intracranial abnormalities. With foetal surgery as a new treatment option, robust intracranial imaging is important for comprehensive preoperative evaluation and prognostication. We aimed to determine the incidence of infratentorial and supratentorial findings detected by magnetic resonance imaging (MRI) alone and MRI compared to ultrasound. METHODS: Two systematic reviews comparing MRI to ultrasound and MRI alone were conducted on MEDLINE, EMBASE, and Cochrane databases identifying studies of foetal OSB from 2000 to 2020. Intracranial imaging findings were analysed at ≤ 26 or > 26 weeks gestation and neonates (≤ 28 days). Data was independently extracted by two reviewers and meta-analysis was performed where possible. RESULTS: Thirty-six studies reported brain abnormalities detected by MRI alone in patients who previously had an ultrasound. Callosal dysgenesis was identified in 4/29 cases (2 foetuses ≤ 26 weeks, 1 foetus under any gestation, and 1 neonate ≤ 28 days) (15.1%, CI:5.7-34.3%). Heterotopia was identified in 7/40 foetuses ≤ 26 weeks (19.8%, CI:7.7-42.2%), 9/36 foetuses > 26 weeks (25.3%, CI:13.7-41.9%), and 64/250 neonates ≤ 28 days (26.9%, CI:15.3-42.8%). Additional abnormalities included aberrant cortical folding and other Chiari II malformation findings such as lower cervicomedullary kink level, tectal beaking, and hypoplastic tentorium. Eight studies compared MRI directly to ultrasound, but due to reporting inconsistencies, it was not possible to meta-analyse. CONCLUSION: MRI is able to detect anomalies hitherto underestimated in foetal OSB which may be important for case selection. In view of increasing prenatal OSB surgery, further studies are required to assess developmental consequences of these findings

A spatio-temporal atlas of the developing fetal brain with spina bifida aperta

Background: Spina bifida aperta (SBA) is a birth defect associated with severe anatomical changes in the developing fetal brain. Brain magnetic resonance imaging (MRI) atlases are popular tools for studying neuropathology in the brain anatomy, but previous fetal brain MRI atlases have focused on the normal fetal brain. We aimed to develop a spatio-temporal fetal brain MRI atlas for SBA. Methods: We developed a semi-automatic computational method to compute the first spatio-temporal fetal brain MRI atlas for SBA. We used 90 MRIs of fetuses with SBA with gestational ages ranging from 21 to 35 weeks. Isotropic and motion-free 3D reconstructed MRIs were obtained for all the examinations. We propose a protocol for the annotation of anatomical landmarks in brain 3D MRI of fetuses with SBA with the aim of making spatial alignment of abnormal fetal brain MRIs more robust. In addition, we propose a weighted generalized Procrustes method based on the anatomical landmarks for the initialization of the atlas. The proposed weighted generalized Procrustes can handle temporal regularization and missing annotations. After initialization, the atlas is refined iteratively using non-linear image registration based on the image intensity and the anatomical land-marks. A semi-automatic method is used to obtain a parcellation of our fetal brain atlas into eight tissue types: white matter, ventricular system, cerebellum, extra-axial cerebrospinal fluid, cortical gray matter, deep gray matter, brainstem, and corpus callosum. Results: An intra-rater variability analysis suggests that the seven anatomical land-marks are sufficiently reliable. We find that the proposed atlas outperforms a normal fetal brain atlas for the automatic segmentation of brain 3D MRI of fetuses with SBA. Conclusions: We make publicly available a spatio-temporal fetal brain MRI atlas for SBA, available here: https://doi.org/10.7303/syn25887675. This atlas can support future research on automatic segmentation methods for brain 3D MRI of fetuses with SBA

Texture-Based Analysis of Fetal Organs in Fetal Growth Restriction

Fetal growth restriction (FGR) is common, affecting around 10% of all pregnancies. Growth restricted fetuses fail to achieve their genetically predetermined size and often weigh <10th centile for gestation. However, even appropriately grown fetuses can be affected, with the diagnosis of FGR missed before birth. Babies with FGR have a higher rate of stillbirth, neonatal morbidity such as breathing problems, and neurodevelopmental delay. FGR is usually due to placental insufficiency leading to poor placental perfusion and fetal hypoxia. MRI is increasingly used to image the fetus and placenta. Here we explore the use of novel multi-compartment Intravoxel Incoherent Motion Model (IVIM)-based models for MRI fetal and placental analysis, to improve understanding of FGR and quantify abnormalities and biomarkers in fetal organs. In 12 normally grown and 12 FGR gestational-age matched pregnancies (Median 28+ 4 wks±3+ 3 wks) we acquired T2 relaxometry and diffusion MRI datasets. Decreased perfusion, pseudo-diffusion coefficient, and fetal blood T2 values in the placenta and fetal liver were significant features distinguishing between FGR and normal controls (p-value <0.05). This may be related to the preferential shunting of fetal blood away from the fetal liver to the fetal brain that occurs in placental insufficiency. These features were used to predict FGR diagnosis and gestational age at delivery using simple machine learning models. Texture analysis was explored to compare Haralick features between control and FGR fetuses, with the placenta and liver yielding the most significant differences between the groups. This project provides insights into the effect of FGR on fetal organs emphasizing the significant impact on the fetal liver and placenta, and the potential of an automated approach to diagnosis by leveraging simple machine learning models

Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Deep neural networks have increased the accuracy of automatic segmentation, however their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function. Our theory also allows us to define the leaf-Dice loss, a label-set generalisation of the Dice loss particularly suited for partial supervision with only missing labels. Using the leaf-Dice loss, we set a new state of the art in partially supervised learning for fetal brain 3D MRI segmentation. We achieve a deep neural network able to segment white matter, ventricles, cerebellum, extra-ventricular CSF, cortical gray matter, deep gray matter, brainstem, and corpus callosum based on fetal brain 3D MRI of anatomically normal fetuses or with open spina bifida. Our implementation of the proposed label-set loss functions is available at https://github.com/LucasFidon/label-set-loss-functions

Label-Set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

Deep neural networks have increased the accuracy of automatic segmentation, however their accuracy depends on the availability of a large number of fully segmented images. Methods to train deep neural networks using images for which some, but not all, regions of interest are segmented are necessary to make better use of partially annotated datasets. In this paper, we propose the first axiomatic definition of label-set loss functions that are the loss functions that can handle partially segmented images. We prove that there is one and only one method to convert a classical loss function for fully segmented images into a proper label-set loss function. Our theory also allows us to define the leaf-Dice loss, a label-set generalisation of the Dice loss particularly suited for partial supervision with only missing labels. Using the leaf-Dice loss, we set a new state of the art in partially supervised learning for fetal brain 3D MRI segmentation. We achieve a deep neural network able to segment white matter, ventricles, cerebellum, extra-ventricular CSF, cortical gray matter, deep gray matter, brainstem, and corpus callosum based on fetal brain 3D MRI of anatomically normal fetuses or with open spina bifida. Our implementation of the proposed label-set loss functions is available at https://github.com/LucasFidon/label-set-loss-functions

Healthcare finance in the Kingdom of Saudi Arabia:a qualitative study of householders’ attitudes

Background: The public sector healthcare system in Saudi Arabia, essentially financed by oil revenues and ‘free at the point of delivery’, is coming under increasing strain due to escalating expenditure and an increasingly volatile oil market and is likely to be unsustainable in the medium to long term. Objectives: This study examines how satisfied the Saudi people are with their public sector healthcare services and assesses their willingness to contribute to financing the system through a national health insurance scheme. The study also examines public preferences and expectations of a future national health insurance system. Methods: A total of 36 heads of households participated in face-to-face audio-recorded semi-structured interviews. The participants were purposefully selected based on different socio-economic and socio-demographic factors from urban and rural areas to represent the geographical diversity that would presumably influence individual views, expectations, preferences and healthcare experiences. Results: The evidence showed some dissatisfaction with the provision and quality of current public sector healthcare services, including the availability of appointments, waiting times and the availability of drugs. The households indicated a willingness to contribute to a national insurance scheme, conditional upon improvements in the quality of public sector healthcare services. The results also revealed a variety of preferences and expectations regarding the proposed national health insurance scheme. Conclusions: Quality improvement is a key factor that could motivate the Saudi people to contribute to financing the healthcare system. A new authority, consisting of a partnership between the public and private sectors under government supervision, could represent an acceptable option for addressing the variation in public preferences

Super-resolution Reconstruction MRI Application in Fetal Neck Masses and Congenital High Airway Obstruction Syndrome

OBJECTIVE: Reliable airway patency diagnosis in fetal tracheolaryngeal obstruction is crucial to select and plan ex utero intrapartum treatment (EXIT) surgery. We compared the clinical utility of magnetic resonance imaging (MRI) super-resolution reconstruction (SRR) of the trachea, which can mitigate unpredictable fetal motion effects, with standard 2-dimensional (2D) MRI for airway patency diagnosis and assessment of fetal neck mass anatomy. STUDY DESIGN: A single-center case series of 7 consecutive singleton pregnancies with complex upper airway obstruction (2013-2019). SETTINGS: A tertiary fetal medicine unit performing EXIT surgery. METHODS: MRI SRR of the trachea was performed involving rigid motion correction of acquired 2D MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume. SRR, 2D MRI, and paired data were blindly assessed by 3 radiologists in 3 experimental rounds. RESULTS: Airway patency was correctly diagnosed in 4 of 7 cases (57%) with 2D MRI as compared with 2 of 7 cases (29%) with SRR alone or paired 2D MRI and SRR. Radiologists were more confident (P = .026) in airway patency diagnosis when using 2D MRI than SRR. Anatomic clarity was higher with SRR (P = .027) or paired data (P = .041) in comparison with 2D MRI alone. Radiologists detected further anatomic details by using paired images versus 2D MRI alone (P < .001). Cognitive load, as assessed by the NASA Task Load Index, was increased with paired or SRR data in comparison with 2D MRI. CONCLUSION: The addition of SRR to 2D MRI does not increase fetal airway patency diagnostic accuracy but does provide improved anatomic information, which may benefit surgical planning of EXIT procedures

Investigating the Willingness to Pay for a Contributory National Health Insurance Scheme in Saudi Arabia:A Cross-sectional Stated Preference Approach

Background: The Saudi Healthcare System is universal, financed entirely from government revenue principally derived from oil, and is ‘free at the point of delivery’ (non-contributory). However, this system is unlikely to be sustainable in the medium to long term. This study investigates the feasibility and acceptability of healthcare financing reform by examining households’ willingness to pay (WTP) for a contributory national health insurance scheme. Methods: Using the contingent valuation method, a pre-tested interviewer-administered questionnaire was used to collect data from 1187 heads of household in Jeddah province over a 5-month period. Multi-stage sampling was employed to select the study sample. Using a double-bounded dichotomous choice with the follow-up elicitation method, respondents were asked to state their WTP for a hypothetical contributory national health insurance scheme. Tobit regression analysis was used to examine the factors associated with WTP and assess the construct validity of elicited WTP. Results: Over two-thirds (69.6%) indicated that they were willing to participate in and pay for a contributory national health insurance scheme. The mean WTP was 50 Saudi Riyal (US$13.33) per household member per month. Tobit regression analysis showed that household size, satisfaction with the quality of public healthcare services, perceptions about financing healthcare, education and income were the main determinants of WTP. Conclusions: This study demonstrates a theoretically valid WTP for a contributory national health insurance scheme by Saudi people. The research shows that willingness to participate in and pay for a contributory national health insurance scheme depends on participant characteristics. Identifying and understanding the main influencing factors associated with WTP are important to help facilitate establishing and implementing the national health insurance scheme. The results could assist policy-makers to develop and set insurance premiums, thus providing an additional source of healthcare financing

Reply to editorial and commentaries on Steele, Al-Mufti, Augustyn, Chandrajith, Coghlan, Coulson et al. (2018) "Cause of Cambrian explosion - Terrestrial or cosmic?"

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