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Implementation and clinical evaluation of an in-house thoracic auto-segmentation model for 0.35 T magnetic resonance imaging guided radiotherapy
No full textBackground and Purpose: Magnetic resonance imaging-guided radiotherapy (MRgRT) facilitates high accuracy, small margins treatments at the cost of time-consuming and labor-intensive manual delineation of organs-at-risk (OARs). Auto-segmentation models show promise in streamlining this workflow. This study investigates the clinical applicability of a set of thoracic OAR segmentation models for baseline treatment planning in lung tumor patients. We investigate the use of the models for treatment at a 0.35 T MR-linac, assess their potential to reduce physician workload in terms of time savings and quantify the extent of required manual corrections, providing insights into the value of their integration into clinical practice. Materials and Methods: Deep-learning based auto-segmentation models for 9 thoracic OARs were integrated into the MRgRT workflow. Two groups of 11 lung cancer cases each were prospectively considered. For Group 1 auto-segmentation contours were corrected by physicians, for Group 2 manual contouring according to standard clinical workflows was performed. Contouring times were recorded for both. Time savings between the groups as well as correlations of the extent of corrections to correction times for Group 1 patients were analyzed. Results: The model performed consistently well across all Group 1 cases. Median contouring times were reduced for six out of nine OARs leading to a reduction of 50.3 % or 12.6 min in median total contouring time. Conclusion: Feasibility of auto-segmentation for baseline treatment planning at the 0.35 T MR-linac was shown with significant time savings demonstrated. Time saving potential could not be estimated from model geometric performance metrics
Out-of-Pocket Expenditure (OOPE) Among COVID-19 Patients by Insurance Status in a Quaternary Hospital in Karnataka, India
No full textOut-of-pocket expenditure (OOPE) comprises 62% of national health expenditure in India. This heavy reliance on direct payments has engendered economic vulnerability and catastrophic financial pressures (typically defined as out-of-pocket spending exceeding a certain threshold of household income, leading to financial hardship) on households in a country where public health spending remains below targeted levels. The onset of the COVID-19 pandemic intensified these financial hardships further, as both total healthcare spending and OOPE experienced significant escalations due to the increased need for emergency care, vaccination efforts, and expanded health infrastructure. A retrospective, single-center study was conducted using data from COVID-19 patients admitted between June 2020 and June 2022. Patient data were collected from the Medical Records, IT, and Finance departments. A validated proforma was used for data extraction. Descriptive statistics were calculated, and the Shapiro-Wilk test was applied to assess normality of billing and OOPE data. Patients were stratified into three groups based on their insurance status, allowing for comparative analysis of OOPE percentages and absolute expenditures. The 2715 COVID-19 patients were categorized into three groups according to their health financing: those covered under AB-PMJAY (42.76%), private health insurance (22.16%), and the uninsured (35%). While the median billing amounts were comparable across these groups (ranging between INR 85,000 and INR 90,000), a substantial disparity was observed in terms of financial burden. All patients covered under AB-PMJAY incurred no OOPE, whereas privately insured patients had a median OOPE that constituted approximately 21% of their total billing amounts, with significant variability among different insurers. The uninsured group represented 35% of the cases and experienced the highest median OOPE, indicating substantial financial risk. The COVID-19 pandemic has revealed critical gaps in India's health financing framework. This study emphasizes the strong financial protection provided by AB-PMJAY, while also exposing the limitations of private health insurance in shielding patients from substantial healthcare costs. As the country progresses toward universal health coverage, there is a pressing need to expand public health insurance schemes that are inclusive, equitable, and effectively implemented. Additionally, strengthening regulation and accountability in the private insurance sector is essential. The study findings reinforce that AB-PMJAY has been highly successful in reducing OOPE and enhancing financial risk protection. Although private insurance reduced OOPE, patients still faced considerable expenses. The stark difference in OOPE of 100% for uninsured patients, 21.16% for privately insured, and 0% for AB-PMJAY beneficiaries underscores the importance of further expanding AB-PMJAY to reach more vulnerable populations
Shared and distinct alterations in brain structure of youth with internalizing or externalizing disorders:Findings from the ENIGMA Antisocial Behavior, ADHD, MDD, and Anxiety Working Groups
No full textBACKGROUND: Externalizing and internalizing disorders are common in youth but are often studied separately, preventing researchers from identifying shared (i.e., transdiagnostic) alterations in brain structure. Using data from the ENIGMA Consortium, we conducted a mega-analysis to identify shared and distinct cortical and subcortical brain alterations across internalizing (anxiety disorders and depression) and externalizing disorders (attention-deficit/hyperactivity disorder [ADHD] and conduct disorder [CD]) in youth. METHODS: 3D T1-weighted MRI data from youth (aged 4-21 years) with anxiety disorders (n=1,044), depression (n=504), ADHD (n=1,317), and CD (n=1,172), along with healthy controls (n=4,743) were analyzed. We assessed group differences in regional cortical thickness, surface area, and subcortical volume using linear models, adjusted for site, age, and sex, and total intracranial volume in the surface area and subcortical volume models. RESULTS: We observed transdiagnostic associations, with both internalizing and externalizing disorders characterized by lower surface area in the insula, entorhinal cortex, and middle temporal gyrus, and lower amygdala volume (Cohen's ds=-0.07 to -0.24), as well as total surface area and intracranial volume (ds=-0.11 to -0.25). Externalizing-specific reductions in surface area were observed in fronto-parietal regions (ds=-0.08 to -0.13), but no internalizing-specific associations were identified. Disorder-specific alterations were identified for ADHD, CD, and anxiety disorders, but not depression. CONCLUSIONS: Both common and disorder-specific alterations were identified, with regions involved in salience attribution and emotion processing implicated across internalizing and externalizing disorders. These findings can guide future research targeting common biological processes across youth psychiatric disorders as well as features unique to individual disorders
Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records:Systematic Review
No full textBACKGROUND: Artificial intelligence (AI)-based diagnostic prediction models could aid primary care (PC) in decision-making for faster and more accurate diagnoses. AI has the potential to transform electronic health records (EHRs) data into valuable diagnostic prediction models. Different prediction models based on EHR have been developed. However, there are currently no systematic reviews that evaluate AI-based diagnostic prediction models for PC using EHR data. OBJECTIVE: This study aims to evaluate the content of diagnostic prediction models based on AI and EHRs in PC, including risk of bias and applicability. METHODS: This systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, Embase, Web of Science, and Cochrane were searched. We included observational and intervention studies using AI and PC EHRs and developing or testing a diagnostic prediction model for health conditions. Two independent reviewers (LH and AC) used a standardized data extraction form. Risk of bias and applicability were assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool). RESULTS: From 10,657 retrieved records, a total of 15 papers were selected. Most EHR papers focused on 1 chronic health care condition (n=11, 73%). From the 15 papers, 13 (87%) described a study that developed a diagnostic prediction model and 2 (13%) described a study that externally validated and tested the model in a PC setting. Studies used a variety of AI techniques. The predictors used to develop the model were all registered in the EHR. We found no papers with a low risk of bias, and high risk of bias was found in 9 (60%) papers. Biases covered an unjustified small sample size, not excluding predictors from the outcome definition, and the inappropriate evaluation of the performance measures. The risk of bias was unclear in 6 papers, as no information was provided on the handling of missing data and no results were reported from the multivariate analysis. Applicability was unclear in 10 (67%) papers, mainly due to lack of clarity in reporting the time interval between outcomes and predictors. CONCLUSIONS: Most AI-based diagnostic prediction models based on EHR data in PC focused on 1 chronic condition. Only 2 papers tested the model in a PC setting. The lack of sufficiently described methods led to a high risk of bias. Our findings highlight that the currently available diagnostic prediction models are not yet ready for clinical implementation in PC
Cross-sectional and longitudinal associations between 24-hour movement behaviors and growth, motor, and social-emotional development in early childhood
No full textBACKGROUND: To enhance evidence on optimal 24-hour movement behaviors (physical activity, sedentary behavior, and sleep) in early childhood, this study investigated cross-sectional and longitudinal associations of the composition of these behaviors with social-emotional development, gross motor development and growth in 0-4-year-olds. METHODS: Data were collected at two timepoints (baseline and 9 months later) in two sub-cohorts from the My Little Moves study: one examining social-emotional development (sub-cohort-SE) and one gross motor development and growth (sub-cohort-GM). Children's time spent in 24-hour movement behaviors was assessed via parent-report using the My Little Moves app. Isometric log-ratios were calculated to represent 24-hour movement behavior composition. Social-emotional and gross motor development were assessed using the Bayley Scales of Infant and Toddler Development-III, with both total raw and norm-referenced scaled scores. Children's weight and height were measured to calculate BMI z-scores. Linear regression and mixed-model analyses examined cross-sectional and longitudinal associations, with significant results further explored using compositional isotemporal reallocation analysis. RESULTS: Sub-cohort-SE provided data from 101 children at timepoint 1 (age 20.6 ± 12.5 months) and 62 children at timepoint 2 (age 25.7 ± 9.8 months). Sub-cohort-GM provided data from 60 children at timepoint 1 (age 20.4 ± 10.8 months) and 46 children at timepoint 2 (age 27.6 ± 9.6 months). The composition of 24-hour movement behaviors was significantly associated with raw gross motor development scores in both cross-sectional (p < .001, R²? = 0.042) and longitudinal (p < .001, R²? = 0.033) analyses. The association with BMI z-scores was significant only in the cross-sectional analysis (p = .015, R²? = 0.130). Reallocating 10 min from sedentary behavior to physical activity or sleep increased raw gross motor development scores by 0.22 (95% CI [0.11, 0.33]), and 0.27 (95% CI [0.08, 0.45]). Reallocating 10 min from sedentary behavior to sleep increased BMI z-scores by 0.04 (95% CI [0.01, 0.06]). CONCLUSIONS: The composition of 24-hour movement behaviors was significantly associated with BMI z-scores and gross motor development, but not social-emotional development in children aged 0-4 years. Evidence on the optimal distribution of movement behaviors remains unclear and needs further examination in larger longitudinal studies
On the neural symphony of speech:decoding speech production with stereo-electroencephalography
Get PDFSpeech is one of the most natural ways for people to share ideas, emotions, and stories. Losing this ability—through a stroke or ALS, for example—can be devastating. This thesis explored the “neural symphony” of speech: how different parts of the brain play together, like instruments in an orchestra, to create sounds, movements, and meaning. By recording brain activity with small electrodes placed deep in the brain, it was shown that speech can be decoded from many different regions. The research also demonstrated how these brain signals can be transformed into sound very fast—an important step toward technology that gives people back their voice. Such systems, called brain-computer interfaces, use a person’s thoughts about speaking to generate artificial speech. The findings in this thesis highlight how understanding the brain’s symphony of speech can both advance neuroscience and help restore communication to those who have lost it
Improving osteoporosis care and fracture prevention with emphasis on adherence:One size does not fit all
No full textExtended confiscation in the Netherlands
No full textThis chapter presents the legal framework on extended confiscation in the Netherlands. Two separate legal regimes have been established which make (extended) confiscation possible. One form of confiscation is classified as a form of punishment, while more far-reaching forms are classified as a measure. Most confiscation will be regarded as a measure, not as a punishment, which explains the low level of opposition to further extensions. The different requirements for confiscation established by these regimes are discussed and evaluated in the light of fundamental rights and general principles of EU law. It is argued that Dutch law has adopted a pragmatic approach to confiscation which allows for a broad scope of confiscation. Despite the wide range of options for confiscation, the legal framework is well established in practice and seen as an important tool in the fight against organised crime
Capital Adjustment, Technology Vintages and Training: Role of capital investment spikes in firm’s skill formation strategy
No full textWe study how investment spikes in technologies and complementary infrastructure influence firms’ hiring and training strategies. While prior work emphasizes how technologies reallocate skill demand, few focus on how firms acquire the required skills. Using linked employer-employee data on German establishments, we identify spikes by their technological composition and capital vintages. Event study estimates show that investment spikes in ICT and production line technologies lead to an upscaling effect raising employment by external hiring followed by training of young apprentices. Combining technologies with factories and plants induces firms to use apprenticeship training without an increase in external hiring. Incumbent workers are trained when investment spikes renew the vintage of firm’s capital. Our findings support a vintage human capital framework in which technology adoption induces firms to gradually adjust workforce through hiring and training while preserving expertise of incumbent workers