S1346 Exploring the Use of Point of Care Ultrasound in Screening for Non-Alcoholic Fatty Liver Disease: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Non-alcoholic fatty liver disease (NAFLD) is a major cause of cirrhosis and liver failure globally. It is estimated that within the next 20 years, NAFLD will be the major cause of liver-related morbidity and mortality as well as the leading indication for liver transplantation. Despite its broad impact, screening recommendations for NAFLD remain varied between gastrointestinal societies. Point of care ultrasound (POCUS) has emerged as a new cost-effective form of screening and diagnosing intrabdominal pathologies. We aimed to estimate the effectiveness of POCUS in screening for NAFLD compared to formal ultrasound when screening by general practitioners trained in ultrasonography. Methods: Data was collected from Cochrane, Pubmed, Embase, and Google Scholar using search terms related to POCUS and NAFLD screening. Observational cross-sectional studies between the years 2010-2023 were included in our analysis. Reviewers evaluated articles for eligibility and extracted data for pooled analysis. The risk of bias was assessed by reviewers using a validated risk of bias assessment tool. Discrepancies between authors were resolved by a third reviewer or by consensus. Statistical analysis included pooled sensitivity, specificity, negative predictive value, and positive predictive value with all associated 95% confidence intervals respectively. Heterogeneity between studies was plotted on a receiver operator characteristic (ROC) curve for analysis. Results: Our review included 3 studies (n = 428) that met our eligibility criteria. The cumulative sensitivity for POCUS examination was 93% (95% CI: 85 – 98%), with a negative predictive value of 99%. Additionally, the specificity of POCUS for the detection of fatty liver disease was 98% (95% CI: 96 – 99%) with a positive predictive value of 91%. Our studies were found to have low heterogeneity when evaluated on ROC curve plotting. Conclusion: Our systematic literature review supports the effectiveness of POCUS as a reliable method for screening for fatty liver disease and NAFLD when compared to formal ultrasound. The comparable diagnostic performance, high sensitivity, and specificity of POCUS in detecting hepatic steatosis make it a promising tool for early detection and intervention in NAFLD. Moreover, its portability, accessibility, and potential cost-effectiveness make POCUS a valuable screening option, particularly in resource-limited settings. Further studies are required to validate the findings of our review on POCUS as a screening method (Figure 1, Table 1).Figure 1.: Study selection flowchart. Table 1. - Overall Characteristics of the Included Studies Author Study Design Country N Average Age in Years (Range) % Male POCUS Operator Training POCUS Device Formal Ultrasound Device Barreiros et al. (2019) Cross-Sectional Germany 300 55 (18 - 96) 53% Certification with the German Society for Ultrasound in Medicine Vscan Dual Probe pocket device (GE Medical Systems, Milwaukee, WI, USA) GE Logiq E9 ultrasound system (GE Medical Systems, Milwaukee, WI, USA) Miles et al. (2019) Cross-Sectional Canada 100 53 (N/A) 55% Certification in POCUS via the Canadian Point of Care Ultrasound Society Undefined Handheld Device using a 5–1 MHz phased-array probe GE Logiq E9 ultrasound system (GE Medical Systems, Milwaukee, WI, USA) Stock et al. (2015) Cross-Sectional Germany 28 68 (29-94) 43% Board certification by the National Ultrasound Society Acuson P10 Portable Ultrasound System (Siemens Medical Solutions, Malvern, PA, USA) Sonoline Antares ultrasound system (Siemens Medical Solutions, Malvern, PA, USA)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".