Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results

Preoperative prediction of rectal Cancer staging combining MRI deep transfer learning, radiomics features, and clinical factors: accurate differentiation from stage T2 to T3

BACKGROUND

This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer.

METHODS

We included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data.

RESULTS

After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the trainingset and 0.920 in the test set.

CONCLUSION

The study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.

Fairness of artificial intelligence in healthcare: review and recommendations

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Dipeptidyl-peptidase 3 and IL-6: potential biomarkers for diagnostics in COVID-19 and association with pulmonary infiltrates

Coronavirus SARS-CoV-2 spread worldwide, causing a respiratory disease known as COVID-19. The aim of the present study was to examine whether Dipeptidyl-peptidase 3 (DPP3) and the inflammatory biomarkers IL-6, CRP, and leucocytes are associated with COVID-19 and able to predict the severity of pulmonary infiltrates in COVID-19 patients versus non-COVID-19 patients. 114 COVID-19 patients and 35 patients with respiratory infections other than SARS-CoV-2 were included in our prospective observational study. Blood samples were collected at presentation to the emergency department. 102 COVID-19 patients and 28 non-COVID-19 patients received CT imaging (19 outpatients did not receive CT imaging). If CT imaging was available, artificial intelligence software (CT Pneumonia Analysis) was used to quantify pulmonary infiltrates. According to the median of infiltrate (14.45%), patients who obtained quantitative CT analysis were divided into two groups (> median: 55 COVID-19 and nine non-COVID-19, ≤ median: 47 COVID-19 and 19 non-COVID-19). DPP3 was significantly elevated in COVID-19 patients (median 20.85 ng/ml, 95% CI 18.34-24.40 ng/ml), as opposed to those without SARS-CoV-2 (median 13.80 ng/ml, 95% CI 11.30-17.65 ng/ml; p < 0.001, AUC = 0.72), opposite to IL-6, CRP (each p = n.s.) and leucocytes (p < 0.05, but lower levels in COVID-19 patients). Regarding binary logistic regression analysis, higher DPP3 concentrations (OR = 1.12, p < 0.001) and lower leucocytes counts (OR = 0.76, p < 0.001) were identified as significant and independent predictors of SARS-CoV-2 infection, as opposed to IL-6 and CRP (each p = n.s.). IL-6 was significantly increased in patients with infiltrate above the median compared to infiltrate below the median both in COVID-19 (p < 0.001, AUC = 0.78) and in non-COVID-19 (p < 0.05, AUC = 0.81). CRP, DPP3, and leucocytes were increased in COVID-19 patients with infiltrate above median (each p < 0.05, AUC: CRP 0.82, DPP3 0.70, leucocytes 0.67) compared to infiltrate below median, opposite to non-COVID-19 (each p = n.s.). Regarding multiple linear regression analysis in COVID-19, CRP, IL-6, and leucocytes (each p < 0.05) were associated with the degree of pulmonary infiltrates, as opposed to DPP3 (p = n.s.). DPP3 showed the potential to be a COVID-19-specific biomarker. IL-6 might serve as a prognostic marker to assess the extent of pulmonary infiltrates in respiratory patients.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.

Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan

This study aimed to determine the optimal conditions to measure the percentage of the area considered as pneumonia (pneumonia volume ratio [PVR]) and the computed tomography (CT) score due to coronavirus disease 2019 (COVID-19) using the Ziostation2 image analysis software (Z2; Ziosoft, Tokyo, Japan), which is popular in Japan, and to evaluate its usefulness for assessing the clinical severity. We included 53 patients (41 men and 12 women, mean age: 61.3 years) diagnosed with COVID-19 using polymerase chain reaction who had undergone chest CT and were hospitalized between January 2020 and January 2021. Based on the COVID-19 infection severity, the patients were classified as mild (n = 38) or severe (n = 15). For 10 randomly selected samples, the PVR and CT scores by Z2 under different conditions and the visual simple PVR and CT scores were compared. The conditions with the highest statistical agreement were determined. The usefulness of the clinical severity assessment based on the PVR and CT scores using Z2 under the determined conditions was statistically evaluated. The best agreement with the visual measurement was achieved by the Z2 measurement condition of ≥-600 HU. The areas under the receiver operating characteristic curves, Youden's index, and the sensitivity, specificity, and p-values of the PVR and CT scores by Z2 were as follows: PVR: 0.881, 18.69, 66.7, 94.7, and <0.001; CT score: 0.77, 7.5, 40, 74, and 0.002, respectively. We determined the optimal condition for assessing the PVR of COVID-19 pneumonia using Z2 and demonstrated that the AUC of the PVR was higher than that of CT scores in the assessment of clinical severity. The introduction of new technologies is time-consuming and expensive; our method has high clinical utility and can be promptly used in any facility where Z2 has been introduced.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound

PURPOSE

This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound.

METHODS

The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection.

RESULT

The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43).

CONCLUSION

The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.

New trend in artificial intelligence-based assistive technology for thoracic imaging

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Clinical applications of artificial intelligence in liver imaging

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.

Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network

This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1−5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.

Semi-quantitative evaluation of chest computed tomography for coronavirus disease 2019 in a critical care unit: A case-control study

The spread of abnormal opacity on chest computed tomography (CT) has been reported as a predictor of coronavirus disease 2019 (COVID-19) severity; however, the relationship between CT findings and prognosis in patients with severe COVID-19 remains unclear. The objective of this study was to evaluate the extent of abnormal opacity on chest CT and its association with prognosis in patients with COVID-19 in a critical care medical center, using a simple semi-quantitative method. This single-center case-control study included patients diagnosed with severe COVID-19 pneumonia who were admitted to a critical care center. The diagnosis of COVID-19 was based on positive results of a reverse transcription polymerase chain reaction test. All patients underwent non-contrast whole-body CT upon admission. Six representative axial chest CT images were selected for each patient to evaluate the extent of lung lesions. The percentage of the area involved in the representative CT images was visually assessed by 2 radiologists and scored on 4-point scale to obtain the bedside CT score, which was compared between patients who survived and those who died using the Mann-Whitney U test. A total of 63 patients were included in this study: 51 survived and 12 died after intensive treatment. The inter-rater reliability of bedside scores between the 2 radiologists was acceptable. The median bedside CT score of the survival group was 12.5 and that of the mortality group was 16.5; the difference between the 2 groups was statistically significant. The degree of opacity can be easily scored using representative CT images in patients with severe COVID-19 pneumonia, without sophisticated software. A greater extent of abnormal opacity is associated with poorer prognosis. Predicting the prognosis of patients with severe COVID-19 could facilitate prompt and appropriate treatment.