AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study

Meng, Fanyang; Kottlors, Jonathan; Shahzad, Rahil; Liu, Haifeng; Fervers, Philipp; Jin, Yinhua; Rinneburger, Miriam; Dou, Le; Weisthoff, Mathilda; Liu, Wenyun; Ni, Mengzhe; Sun, Ye; An, Liying; Huai, Xiaochen; Móré, Dorottya; Giannakis, Athanasios; Kaltenborn, Isabel; Bucher, Andreas; Maintz, David; Zhang, Lei; Thiele, Frank; Li, Mingyang; Perkuhn, Michael; Zhang, Huimao; Persigehl, Thorsten

doi:10.1007/s00330-022-09335-9

Cited by 7 publications

(3 citation statements)

References 28 publications

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“… 45 Existing studies mostly focus on the diagnosis of specific pathogens, which is crucial for decision-making regarding specific types of pneumonia in pandemics. 46 , 47 , 48 , 49 A deep learning-based automated detection algorithm developed using 60,989 CXR scans was used to identify active PTB and performed better than human experts. 50 Another AI model based on a multicountry chest CT dataset automatically located the parietal pleura and lung parenchyma and classified COVID-19 pneumonia patients with an accuracy of 90.8%.…”

Section: Discussionmentioning

confidence: 99%

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Shao,

Ma,

et al. 2024

The Innovation

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Section: Discussionmentioning

confidence: 99%

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Shao,

Ma,

et al. 2024

The Innovation

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“…Such a development would be a signi cant leap forward in healthcare delivery, aligning with recent research demonstrating AI's ability to reduce workload and enhance diagnostic accuracy (Studies demonstrating AI's impact on radiological e ciency). Studies, such as that by Meng F et al [71], directly measure how AI can speed up this reporting process, nding that there was a signi cant improvement in reporting time when Radiologists are assisted by AI software (p < 0.01). While Meng F et al's study focusses on the detection of community acquired pneumonia, the principles are universal.…”

Section: In Uence Analysis and Linear Regressionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning for Risk Prediction and Diagnosis of Vertebral Fractures: A Systematic Review and Meta-Analysis

Namireddy,

Gill,

Peerbhai

et al. 2024

Preprint

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Introduction: With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. Method A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. Results AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. Conclusion AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.

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“…Radiological images, acquired using diverse modalities, are subjected to data preprocessing to ensure optimal quality. 2 3 AI algorithms, including machine learning and deep learning models, facilitate precise localization of anatomical structures or pathological lesions. Subsequent analysis of the segmented regions allows for automated detection, characterization, and quantitative measurements, aiding radiologists in making informed diagnostic decisions.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes

Sarangi,

Narayan,

Mohakud

et al. 2023

Indian J Radiol Imaging

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Background The field of radiology relies on accurate interpretation of medical images for effective diagnosis and patient care. Recent advancements in artificial intelligence (AI) and natural language processing have sparked interest in exploring the potential of AI models in assisting radiologists. However, limited research has been conducted to assess the performance of AI models in radiology case interpretation, particularly in comparison to human experts. Objective This study aimed to evaluate the performance of ChatGPT, Google Bard, and Bing in solving radiology case vignettes (Fellowship of the Royal College of Radiologists 2A [FRCR2A] examination style questions) by comparing their responses to those provided by two radiology residents. Methods A total of 120 multiple-choice questions based on radiology case vignettes were formulated according to the pattern of FRCR2A examination. The questions were presented to ChatGPT, Google Bard, and Bing. Two residents wrote the examination with the same questions in 3 hours. The responses generated by the AI models were collected and compared to the answer keys and explanation of the answers was rated by the two radiologists. A cutoff of 60% was set as the passing score. Results The two residents (63.33 and 57.5%) outperformed the three AI models: Bard (44.17%), Bing (53.33%), and ChatGPT (45%), but only one resident passed the examination. The response patterns among the five respondents were significantly different (p = 0.0117). In addition, the agreement among the generative AI models was significant (intraclass correlation coefficient [ICC] = 0.628), but there was no agreement between the residents (Kappa = –0.376). The explanation of generative AI models in support of answer was 44.72% accurate. Conclusion Humans exhibited superior accuracy compared to the AI models, showcasing a stronger comprehension of the subject matter. All three AI models included in the study could not achieve the minimum percentage needed to pass an FRCR2A examination. However, generative AI models showed significant agreement in their answers where the residents exhibited low agreement, highlighting a lack of consistency in their responses.

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AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study

Cited by 7 publications

References 28 publications

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Artificial Intelligence and Machine Learning for Risk Prediction and Diagnosis of Vertebral Fractures: A Systematic Review and Meta-Analysis

Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes

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