ArticleBiomedicines2023
Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 17 citations in OpenAlex.
- Has computer-aided diagnosis now outperformed radiologists in the accurate localization of lung nodules?-a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- Pediatric-specific computer-aided detection of lung nodules in computed tomography scans.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- Surgical Aspects of Treatment of the Lung Cancer Found in Low-Dose CT-Based Screenings.Journal of clinical medicine · 2026Article
- Pediatric pulmonary nodules: current state of knowledge, AI applications, and future directions.Pediatric radiology · 2026Review
- Construction and Validation of an Automatic Segmentation Method for Respiratory Sound Time Labels.The clinical respiratory journal · 2025Article
- Lessons Learned From International Lung Cancer Screening Trials; People at Risk Deserve Screening for Early Detection.Respirology (Carlton, Vic.) · 2025Review
- Computed tomography-guided percutaneous biopsy for diagnosing pulmonary nodules: a prospective, multicenter observational study.Journal of thoracic disease · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Diagnostic accuracy of artificial intelligence-assisted radiology assessment of cancer: a systematic review.BJR artificial intelligence · 2025Article
- Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.European radiology experimental · 2024Article
- Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice-a simulation study.BJR artificial intelligence · 2024Article
- ERS International Congress 2023: highlights from the Clinical Techniques, Imaging and Endoscopy Assembly.ERJ open research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
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.