ArticlePloS one2021
Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation.Journal of clinical medicine · 2026Review
- SpaceTime-SonoNet: efficient classification of ultra-sound video sequences.Medical & biological engineering & computing · 2026Article
- Diagnostic Applications of Artificial Intelligence in Liver Diseases.Journal of clinical medicine · 2025Review
- Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.BMJ open gastroenterology · 2025Review
- Automatic Diagnosis of Hepatocellular Carcinoma and Metastases Based on Computed Tomography Images.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.Scientific reports · 2024Article
- Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence.Journal of hepatocellular carcinoma · 2024Review
- Diagnosis of focal liver lesions from ultrasound images using a pretrained residual neural network.Journal of applied clinical medical physics · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Application of artificial intelligence in the diagnosis of hepatocellular carcinoma.eGastroenterology · 2023Review
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
- Review
- Ultrasound-based artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2022Review
- Artificial intelligence in liver ultrasound.World journal of gastroenterology · 2022Review
- The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos.Scientific reports · 2022Article
- Performance of Deep Learning-Based Algorithm for Detection of Pediatric Intussusception on Abdominal Ultrasound Images.Gastroenterology research and practice · 2022Article
- Challenges and Future Perspectives for Artificial Intelligence in Hepatology: Breaking Barriers for Better Care.Journal of clinical and experimental hepatologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) using a convolutional neural network (CNN) has demonstrated promising performance in radiological analysis. We aimed to develop and validate a CNN for the detection and diagnosis of focal liver lesions (FLLs) from ultrasonography (USG) still images. The CNN was developed with a supervised training method using 40,397 retrospectively collected images from 3,487 patients, including 20,432 FLLs (hepatocellular carcinomas (HCCs), cysts, hemangiomas, focal fatty sparing, and focal fatty infiltration). AI performance was evaluated using an internal test set of 6,191 images with 845 FLLs, then externally validated using 18,922 images with 1,195 FLLs from two additional hospitals. The internal evaluation yielded an overall detection rate, diagnostic sensitivity and specificity of 87.0% (95%CI: 84.3-89.6), 83.9% (95%CI: 80.3-87.4), and 97.1% (95%CI: 96.5-97.7), respectively. The CNN also performed consistently well on external validation cohorts, with a detection rate, diagnostic sensitivity and specificity of 75.0% (95%CI: 71.7-78.3), 84.9% (95%CI: 81.6-88.2), and 97.1% (95%CI: 96.5-97.6), respectively. For diagnosis of HCC, the CNN yielded sensitivity, specificity, and negative predictive value (NPV) of 73.6% (95%CI: 64.3-82.8), 97.8% (95%CI: 96.7-98.9), and 96.5% (95%CI: 95.0-97.9) on the internal test set; and 81.5% (95%CI: 74.2-88.8), 94.4% (95%CI: 92.8-96.0), and 97.4% (95%CI: 96.2-98.5) on the external validation set, respectively. CNN detected and diagnosed common FLLs in USG images with excellent specificity and NPV for HCC. Further development of an AI system for real-time detection and characterization of FLLs in USG is warranted.
Indexed as
Identifiers
What OpenQuestion holds
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.