Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7
In conclusion, YOLOv7 outperformed YOLOv3 in car ies detection and increasing the image size did not enhance the model’s performance. Elevating the IoU from 50% to 75% and confidence threshold from 0.001 to 0.5 led to a reduction of the model’s performance, while simultaneously improving precision and reducing recall (minimizing false positive s and negatives) for carious lesion detection in bitewing radiographs. (Source: Journal of Digital Imaging)
Source: Journal of Digital Imaging - August 28, 2023 Category: Radiology Source Type: research
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
AbstractLarge numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels due to limitations of human annotations. The purpose of our study was to develop an automated labeling approach that improves the image classification model to distinguish normal knee images from those with abnormalities or prior arthroplasty. The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled dat...
Source: Journal of Digital Imaging - August 24, 2023 Category: Radiology Source Type: research
Assessing Available Open-Source PACS Options
AbstractMedical imaging technology is producing a growing number of medical images types as well as patient-related information. The benefits of using modern medical imaging systems in healthcare are undeniable. Picture archiving and communication system (PACS) have revolutionized medical imaging practice. PACS have widely impacted the accessibility of medical images, reduced imaging costs, eliminated the physical storage of films, improved time management of radiologists, and allowed automated decision-making and diagnosis. Many health organizations and manufacturers have invested on developing commercial PACS. However, c...
Source: Journal of Digital Imaging - August 17, 2023 Category: Radiology Source Type: research
Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study
This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-b...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation
AbstractThyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5 –7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnostic accuracy and workflow efficiency when integrated into clinical decision pathways. Previous studies have evaluated AI systems against physicians, whereas we aim to compare the benefits of incorporating AI into their final diagnostic decision. This work analyzed the potential for artificial intelligence (AI)-based de...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results
This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images in abdominal dual-energy computed tomography (DECT). Sixty-two patients [mean age ± standard deviation (SD): 56 years ± 13; 30 men] who underwent abdominal DECT were prospectively included in this study. The 70-keV DECT images in the portal phase were reconstructed at 5-mm and 1.25-mm slice thicknesses with 40% ASIR-V (ASIR-V40%) and at 1.25-mm slice with deep learn...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study
This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-b...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation
AbstractThyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5 –7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnostic accuracy and workflow efficiency when integrated into clinical decision pathways. Previous studies have evaluated AI systems against physicians, whereas we aim to compare the benefits of incorporating AI into their final diagnostic decision. This work analyzed the potential for artificial intelligence (AI)-based de...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results
This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images in abdominal dual-energy computed tomography (DECT). Sixty-two patients [mean age ± standard deviation (SD): 56 years ± 13; 30 men] who underwent abdominal DECT were prospectively included in this study. The 70-keV DECT images in the portal phase were reconstructed at 5-mm and 1.25-mm slice thicknesses with 40% ASIR-V (ASIR-V40%) and at 1.25-mm slice with deep learn...
Source: Journal of Digital Imaging - August 14, 2023 Category: Radiology Source Type: research
Are the Pilots Onboard? Equipping Radiologists for Clinical Implementation of AI
AbstractThe incorporation of artificial intelligence into radiological clinical workflow is on the verge of being realized. To ensure that these tools are effective, measures must be taken to educate radiologists on tool performance and failure modes. Additionally, radiology systems should be designed to avoid automation bias and the potential decline in radiologist performance. Designed solutions should cater to every level of expertise so that patient care can be enhanced and risks reduced. Ultimately, the radiology community must provide education so that radiologists can learn about algorithms, their inputs and outputs...
Source: Journal of Digital Imaging - August 9, 2023 Category: Radiology Source Type: research
CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations
We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional fea ture selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical ill...
Source: Journal of Digital Imaging - August 8, 2023 Category: Radiology Source Type: research
