ArticleRadiology2022
Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.
Article in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 42 citations in OpenAlex.
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement.Abdominal radiology (New York) · 2025Article
- Evaluation of artificial-intelligence-based liver segmentation and its application for longitudinal liver volume measurement.Abdominal radiology (New York) · 2025Article
- Methodology for a fully automated pipeline of AI-based body composition tools for abdominal CT.Abdominal radiology (New York) · 2025Article
- Artificial Intelligence-based Liver Volume Measurement Using Preoperative and Postoperative CT Images.Current medical imaging · 2025Article
- Development and validation of an interpretable machine learning model for standard spleen volume prediction.Quantitative imaging in medicine and surgery · 2025Article
- Fully Automated and Explainable Measurement of Liver Surface Nodularity in CT: Utility for Staging Hepatic Fibrosis.Academic radiology · 2025Article
- Systematic Review: AI Applications in Liver Imaging with a Focus on Segmentation and Detection.Life (Basel, Switzerland) · 2025Review
- Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.European radiology · 2025Article
- Hepatic Arterial Infusion Chemotherapy for Hepatocellular Carcinoma: A Three-Dimensional Visualization Perspective.Journal of hepatocellular carcinoma · 2025Article
- AI in imaging: the regulatory landscape.The British journal of radiology · 2024Article
- Noninvasive prediction of insufficient biochemical response after ursodeoxycholic acid treatment in patients with primary biliary cholangitis based on pretreatment nonenhanced MRI.European radiology · 2024Article
- Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence.World journal of transplantation · 2023Review
- CT and MR imaging of primary biliary cholangitis: a pictorial review.Insights into imaging · 2023Review
- A vendor-agnostic, PACS integrated, and DICOMcompatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.Research square · 2023Article
- Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Computed tomography-based measurements of normative liver and spleen volumes in children.Pediatric radiology · 2023Article
- Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements.Frontiers in medicine · 2023Article
- A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.Frontiers in medicine · 2023Article
- Spleen volume-based non-invasive tool for predicting hepatic decompensation in people with compensated cirrhosis (CHESS1701).JHEP reports : innovation in hepatology · 2022Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.