Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography

Machado, Marcos; Silva, Ronnyldo R. E.; Namías, Mauro; Lessa, Andreia S.; Neves, Margarida C. L. C.; Silva, Carolina T. A.; Oliveira, Danillo Menezes; Reina, Thamiris Rosado; Lira, Arquimedes A. B.; Almeida, Leandro; Zanchettin, Cleber; Netto, Eduardo Martins

doi:10.1007/s40846-023-00781-4

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our proposed model further improved these outcomes, achieving a Dice coefficient of 0.743 (95% CI: 0.657–0.826) in the validation set and 0.723 (95% CI: 0.602–0.845) in the test set, indicating superior performance in lesion segmentation. Machado et al ( 31 ) utilized 2D Inf-Net for auto-segmentation of COVID-19 and other types of pneumonia using CT scans. The mean F1 score of the auto-segmentation algorithm was 0.72, similar to our results.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information

Zhu,

Che,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Pneumonia can be anatomically classified into lobar, lobular, and interstitial types, with each type associated with different pathogens. Utilizing artificial intelligence (AI) to determine the anatomical classifications of pneumonia and assist in refining the differential diagnosis may offer a more viable and clinically relevant solution. This study aimed to develop a multi-classification model capable of identifying the occurrence of pneumonia in patients by utilizing case-specific computed tomography (CT) information, categorizing the pneumonia type (lobar, lobular, and interstitial pneumonia), and performing segmentation of the associated lesions. Methods A total of 61 lobar pneumonia patients, 60 lobular pneumonia patients, and 60 interstitial pneumonia patients were consecutively enrolled at our local hospital from June 2020 and May 2022. All selected cases were divided into a training cohort (n=135) and an independent testing cohort (n=46). To generate the ground truth labels for the training process, manual segmentation and labeling were performed by three junior radiologists. Subsequently, the segmentations were manually reviewed and edited by a senior radiologist. AI models were developed to automatically segment the infected lung regions and classify the pneumonia. The accuracy of pneumonia lesion segmentation was analyzed and evaluated using the Dice coefficient. Receiver operating characteristic curves were plotted, and the area under the curve (AUC), accuracy, precision, sensitivity, and specificity were calculated to assess the efficacy of pneumonia classification. Results Our AI model achieved a Dice coefficient of 0.743 [95% confidence interval (CI): 0.657–0.826] for lesion segmentation in the training set and 0.723 (95% CI: 0.602–0.845) in the test set. In the test set, our model achieved an accuracy of 0.927 (95% CI: 0.876–0.978), precision of 0.889 (95% CI: 0.827–0.951), sensitivity of 0.889 (95% CI: 0.827–0.951), specificity of 0.946 (95% CI: 0.902–0.990), and AUC of 0.989 (95% CI: 0.969–1.000) for pneumonia classification. We trained the model using labels annotated by senior physicians and compared it to a model trained using labels annotated by junior physicians. The Dice coefficient of the model’s segmentation improved by 0.014, increasing from 0.709 (95% CI: 0.589–0.830) to 0.723 (95% CI: 0.602–0.845), and the AUC improved by 0.042, rising from 0.947 to 0.989. Conclusions Our study presents a robust multi-task learning model with substantial promise in enhancing the segmentation and classification of pneumonia in medical imaging.

show abstract

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information

Zhu,

Che,

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

Artificial Intelligence in Healthcare: Systematic Review of Diagnostic and Screening Tools in Brazil's Resource-Limited Settings (Preprint)

Mancini,

Torres,

Coelho

et al. 2024

Preprint

View full text Add to dashboard Cite

BACKGROUND Artificial Intelligence (AI) has the potential to transform global healthcare, with extensive application in Brazil, particularly for diagnosis (D) and screening (S). OBJECTIVE This study aimed to conduct a systematic review to understand AI applications in Brazilian healthcare, especially focusing on the resource-constrained environments. METHODS A systematic review was performed. The search strategy included the following databases: PubMed, Cochrane Library, Embase, Web of Science, LILACS, and SciELO. The search covered articles from 1993 to November 2023, with 25 articles selected for the final sample. Meta-analysis data were evaluated based on three main metrics: ROC curve, sensitivity, and specificity. A random effects model was applied for each metric to address study variability. RESULTS Key specialties for AI tools include ophthalmology and infectious disease, with a significant concentration of studies conducted in São Paulo state (52%). All articles included testing to evaluate and validate the tools; however, only two conducted secondary testing with a different population. In terms of risk of bias, 10 articles (40%) had medium risk, 8 articles (32%) had low risk, and 7 articles (28%) had high risk. Most studies were public initiatives, totaling 17 (68%), while 5 (20%) were private. In developing countries like Brazil, minimum technological requirements for implementing AI in healthcare must be carefully considered due to financial limitations and often insufficient technological infrastructure. Of the articles reviewed, 76% used computers, and 72% required the Windows operating system. The most used AI algorithm was Machine Learning (44%). The combined sensitivity was 0.8113, the combined specificity was 0.7417, and the combined area under the ROC curve (AUC) was 0.8308. CONCLUSIONS There is a relative balance in the use of both diagnostic and screening tools, with widespread application across Brazil in varied contexts. The need for secondary testing highlights opportunities for future research.

show abstract

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography

Cited by 2 publications

References 20 publications

Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information

Artificial intelligence for segmentation and classification of lobar, lobular, and interstitial pneumonia using case-specific CT information

Artificial Intelligence in Healthcare: Systematic Review of Diagnostic and Screening Tools in Brazil's Resource-Limited Settings (Preprint)

Contact Info

Product

Resources

About