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Asian Spine Journal Jun 2024An experimental study.
STUDY DESIGN
An experimental study.
PURPOSE
This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging.
OVERVIEW OF LITERATURE
In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%-15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made.
METHODS
This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME's graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation.
RESULTS
The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model's accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures.
CONCLUSIONS
The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.
PubMed: 38917858
DOI: 10.31616/asj.2023.0259 -
Respirology Case Reports Jun 2024Bronchial Dieulafoy's disease (BDD), remains poorly understood, with only 88 cases reported globally. Herein, we present the largest case series ( = 7) from a single...
Bronchial Dieulafoy's disease (BDD), remains poorly understood, with only 88 cases reported globally. Herein, we present the largest case series ( = 7) from a single centre, between 2017 and 2023, retrospectively reviewed, detailing clinical presentations, diagnoses, management and up to 4-year follow-up outcomes. Diagnosis relied on characteristic lesions detected through white light bronchoscopy with or without endobronchial ultrasound (EBUS) or narrow band imaging (NBI), along with computed tomography (CT) scans or bronchial angiography. Identification of aberrant vessels beneath lesions and bronchoscopy details were documented. Treatment modalities and follow-up outcomes until December 2023 were noted. All patients were non-smokers. Review of imaging findings by an experienced radiologist was crucial in suspected cases due to risk of bleeding and often unconclusive results from biopsy. Management of BDD varied, with six patients undergoing bronchial artery embolization (BAE) and one requiring lobectomy; four patients received additional endobronchial therapy, one died due to malignancy, none experienced recurrence of haemoptysis. Identifying patients with large volume haemoptysis disproportionate to parenchymal disease in CT scans is important. A bronchoscopic surveillance is crucial to avoid biopsy; it can be confirmed using EBUS of NBI. While no established guidelines exist, BAE and endobronchial therapy emerge as valuable interventions, with surgical resection reserved for recurrent cases.
PubMed: 38915736
DOI: 10.1002/rcr2.1411 -
Journal of the Belgian Society of... 2024Renal failure is relatively common in children presenting to the emergency department, suggesting that the assumption of normal renal function is not always valid....
Renal failure is relatively common in children presenting to the emergency department, suggesting that the assumption of normal renal function is not always valid. Although some computed tomography (CT) scans necessitate the use of intravenous contrast, one should probably consider whether a blood test is necessary to assess the patient's renal function and possibly consider other imaging modalities before proceeding. With no pediatric-specific guidelines and no validated pediatric prevention strategies, further research is needed to establish clear recommendations for contrast-enhanced exams in stable and unstable pediatric patients with unknown renal function.
PubMed: 38915315
DOI: 10.5334/jbsr.3271 -
The Indian Journal of Radiology &... Jul 2024A widely accepted set of imaging criteria or classification has not yet been adopted to evaluate response to treatment by percutaneous sclerotherapy for aneurysmal...
A widely accepted set of imaging criteria or classification has not yet been adopted to evaluate response to treatment by percutaneous sclerotherapy for aneurysmal bone cyst (ABC). In this article, we described and illustrated the Royal Orthopaedic Hospital (ROH) scoring system which is a new, reproducible, and objective tool to evaluate the radiological response. We also reported our institutional experience in the efficacy of computed tomography (CT)-guided sclerotherapy for treating such lesions. A retrospective analysis was conducted for 19 patients who underwent CT-guided sclerotherapy with doxycycline and albumin to treat ABC. Follow-up magnetic resonance imaging, at a minimum of 12 months, was assessed according to the four ROH scoring system parameters: cystic component, fluid-fluid level, presence of consolidation, and cortical integrity. The cumulative score was used to grade response as either: excellent, good, equivocal, or poor. Out of 19 patients with a mean age of 17.8 years, 11 cases occurred in the long bones, 5 cases in the pelvis, and 1 in each of the C3 vertebral body, scapula, and talus. The mean parameter of response score for cystic component was 2, fluid-fluid level was 1.3, consolidation was 2, and cortical integrity was 2.1. Four cases showed excellent response, 12 cases showed good response, 2 cases showed equivocal response, and 1 case showed poor response. Interrater reliability was excellent (κ = 0.9). The ROH scoring system provides the radiologist and surgeon with an objective method to score imaging parameters of response independently and achieve a grade based on the cumulative score.
PubMed: 38912248
DOI: 10.1055/s-0044-1779266 -
The Indian Journal of Radiology &... Jul 2024Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely... (Review)
Review
Although abundant literature is currently available on the use of deep learning for breast cancer detection in mammography, the quality of such literature is widely variable. To evaluate published literature on breast cancer detection in mammography for reproducibility and to ascertain best practices for model design. The PubMed and Scopus databases were searched to identify records that described the use of deep learning to detect lesions or classify images into cancer or noncancer. A modification of Quality Assessment of Diagnostic Accuracy Studies (mQUADAS-2) tool was developed for this review and was applied to the included studies. Results of reported studies (area under curve [AUC] of receiver operator curve [ROC] curve, sensitivity, specificity) were recorded. A total of 12,123 records were screened, of which 107 fit the inclusion criteria. Training and test datasets, key idea behind model architecture, and results were recorded for these studies. Based on mQUADAS-2 assessment, 103 studies had high risk of bias due to nonrepresentative patient selection. Four studies were of adequate quality, of which three trained their own model, and one used a commercial network. Ensemble models were used in two of these. Common strategies used for model training included patch classifiers, image classification networks (ResNet in 67%), and object detection networks (RetinaNet in 67%). The highest reported AUC was 0.927 ± 0.008 on a screening dataset, while it reached 0.945 (0.919-0.968) on an enriched subset. Higher values of AUC (0.955) and specificity (98.5%) were reached when combined radiologist and Artificial Intelligence readings were used than either of them alone. None of the studies provided explainability beyond localization accuracy. None of the studies have studied interaction between AI and radiologist in a real world setting. While deep learning holds much promise in mammography interpretation, evaluation in a reproducible clinical setting and explainable networks are the need of the hour.
PubMed: 38912238
DOI: 10.1055/s-0043-1775737 -
Journal of Multidisciplinary Healthcare 2024Head trauma (HT) in pediatric patients is the number one cause of mortality and morbidity in children. Although computer tomography (CT) imaging provides ample... (Review)
Review
Head trauma (HT) in pediatric patients is the number one cause of mortality and morbidity in children. Although computer tomography (CT) imaging provides ample information in assessing acute traumatic brain injuries (TBIs), there are instances when magnetic resonance imaging (MRI) is needed. Due to its high sensitivity in diagnosing small bleeds, MRI offers a well-documented evaluation of primary acute TBIs. Our pictorial essay aims to present some of the latest imaging protocols employed in head trauma and review some practical considerations. Injury mechanisms in accidental HT, lesions' topography, and hematoma signal variability over time are also discussed. Acute primary intra- and extra-axial lesions and their MRI aspect are showcased using images from patients in our hospital. This pictorial essay has an educational purpose. It is intended to guide young emergency and intensive care unit doctors, neurologists, and neurosurgeons in diagnosing acute primary TBIs on MRI while waiting for the official radiologist's report. The presentation focuses on the most frequent traumatic lesions encountered in acute pediatric head trauma.
PubMed: 38911614
DOI: 10.2147/JMDH.S466044 -
Cureus May 2024Patients presenting with ascites should be properly evaluated to differentiate potential etiologies. Then, based on the evaluation, we can tailor more accurate treatment...
Patients presenting with ascites should be properly evaluated to differentiate potential etiologies. Then, based on the evaluation, we can tailor more accurate treatment plans for patients. Cirrhosis is the most common cause, and others include cancer, heart failure, and, in our case, rarely a visceral artery rupture. Rupture of the splenic artery aneurysm can be lethal and should be considered as a possible differential in a patient with no previous history of heart failure, cancer, or cirrhosis. Our patient was identified after an initial misdiagnosis of possible ascites secondary to cirrhosis. However, input from an interventional radiologist led to proper identification and tailored management. Early treatment is crucial to prevent complications, including death.
PubMed: 38910669
DOI: 10.7759/cureus.60868 -
Magnetic Resonance in Medical Sciences... Jun 2024To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo...
PURPOSE
To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol.
METHODS
This retrospective study included 42 patients (mean age, 70.2 years) with pancreatic cancer who underwent gadoxetic acid-enhanced MRI. Three fat-suppressed T2WI, including conventional fast-spin echo with 6 mm thickness (FSE 6 mm), single-shot fast-spin echo with 6 mm and 3 mm thickness (SSFSE 6 mm and SSFSE 3 mm), were acquired for each patient. For quantitative analysis, the SNRs of the upper abdominal organs were calculated between images with and without DLIR. The pancreas-to-lesion contrast on DLIR images was also calculated. For qualitative analysis, two abdominal radiologists independently scored the image quality on a 5-point scale in the FSE 6 mm, SSFSE 6 mm, and SSFSE 3 mm with DLIR.
RESULTS
The SNRs significantly improved among the three T2-weighted images with DLIR compared to those without DLIR in all patients (P < 0.001). The pancreas-to-lesion contrast of SSFSE 3 mm was higher than those of the FSE 6 mm (P < 0.001) and tended to be higher than SSFSE 6 mm (P = 0.07). SSFSE 3 mm had the highest image qualities regarding pancreas edge sharpness, pancreatic duct clarity, and overall image quality, followed by SSFSE 6 mm and FSE 6 mm (P < 0.0001).
CONCLUSION
SSFSE 3 mm with DLIR demonstrated significant improvements in SNRs of the pancreas, pancreas-to-lesion contrast, and image quality more efficiently than did SSFSE 6 mm and FSE 6 mm. Thin-slice fat-suppressed single-shot T2WI with DLIR can be easily implemented for pancreatic MR protocol.
PubMed: 38910138
DOI: 10.2463/mrms.mp.2024-0017 -
Academic Radiology Jun 2024Cardiovascular CT is required for planning transcatheter aortic valve implantation (TAVI).
BACKGROUND
Cardiovascular CT is required for planning transcatheter aortic valve implantation (TAVI).
PURPOSE
To compare image quality, suitability for TAVI planning, and radiation dose of photon-counting CT (PCCT) with that of dual-source CT (DSCT).
MATERIAL AND METHODS
Retrospective study on consecutive TAVI candidates with aortic valve stenosis who underwent contrast-enhanced aorto-ilio-femoral PCCT and/or DSCT between 01/2022 and 07/2023. Signal-to-noise (SNR) and contrast-to-noise ratio (CNR) were calculated by standardized ROI analysis. Image quality and suitability for TAVI planning were assessed by four independent expert readers (two cardiac radiologists, two cardiologists) on a 5-point-scale. CT dose index (CTDI) and dose-length-product (DLP) were used to calculate effective radiation dose (eRD).
RESULTS
300 patients (136 female, median age: 81 years, IQR: 76-84) underwent 302 CT examinations, with PCCT in 202, DSCT in 100; two patients underwent both. Although SNR and CNR were significantly lower in PCCT vs. DSCT images (33.0 ± 10.5 vs. 47.3 ± 16.4 and 47.3 ± 14.8 vs. 59.3 ± 21.9, P < .001, respectively), visual image quality was higher in PCCT vs. DSCT (4.8 vs. 3.3, P < .001), with moderate overall interreader agreement among radiologists and among cardiologists (κ = 0.60, respectively). Image quality was rated as "excellent" in 160/202 (79.2%) of PCCT vs. 5/100 (5%) of DSCT cases. Readers found images suitable to depict the aortic valve hinge points and to map the femoral access path in 99% of PCCT vs. 85% of DSCT (P < 0.01), with suitability ranked significantly higher in PCCT vs. DSCT (4.8 vs. 3.3, P < .001). Mean CTDI and DLP, and thus eRD, were significantly lower for PCCT vs. DSCT (22.4 vs. 62.9; 519.4 vs. 895.5, and 8.8 ± 4.5 mSv vs. 15.3 ± 5.8 mSv; all P < .001).
CONCLUSION
PCCT improves image quality, effectively avoids non-diagnostic CT imaging for TAVI planning, and is associated with a lower radiation dose compared to state-of-the-art DSCT. Radiologists and cardiologists found PCCT images more suitable for TAVI planning.
PubMed: 38906782
DOI: 10.1016/j.acra.2024.06.014 -
Medicine Jun 2024The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve... (Observational Study)
Observational Study
The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses. Three models (Resnet50, Resnet101, and DenseNet) were used for pneumoconiosis classification based on 1250 chest X-ray images. Three experienced and highly qualified physicians read the collected digital radiography images and classified them from category 0 to category III in a double-blinded manner. The results of the 3 physicians in agreement were considered the relative gold standards. Subsequently, 3 models were used to train and test these images and their performance was evaluated using multi-class classification metrics. We used kappa values and accuracy to evaluate the consistency and reliability of the optimal model with clinical typing. The results showed that ResNet101 was the optimal model among the 3 convolutional neural networks. The AUC of ResNet101 was 1.0, 0.9, 0.89, and 0.94 for detecting pneumoconiosis categories 0, I, II, and III, respectively. The micro-average and macro-average mean AUC values were 0.93 and 0.94, respectively. The accuracy and Kappa values of ResNet101 were 0.72 and 0.7111 for quadruple classification and 0.98 and 0.955 for dichotomous classification, respectively, compared with the relative standard classification of the clinic. This study develops a deep learning based model for screening and staging of pneumoconiosis is using chest radiographs. The ResNet101 model performed relatively better in classifying pneumoconiosis than radiologists. The dichotomous classification displayed outstanding performance, thereby indicating the feasibility of deep learning techniques in pneumoconiosis screening.
Topics: Humans; Pneumoconiosis; Deep Learning; Radiography, Thoracic; Male; Middle Aged; Reproducibility of Results; Female; Diagnosis, Computer-Assisted; Aged; Neural Networks, Computer
PubMed: 38905434
DOI: 10.1097/MD.0000000000038478