Computed Tomography In Diagnosis Research Articles

A deep learning-based system for accurate diagnosis of pelvic bone tumors

Bone image analysis and categorizing bone cancers have both seen advancements thanks to deep learning (DL), more notably convolution neural networks (CNN). This study suggests a brand-new CNN-based methodology for categorizing pelvic bone tumors specifically. This work aims to create a pelvic bone computed tomography (CT) image categorization system based on deep learning. The proposed technique uses a convolutional neural network (CNN) architecture to automatically extract information from the CT images and classify them into distinct categories of tumors. A total of 178 3D CT pictures was discovered and added retroactively. DenseNet created the image-based model with Adam optimizer and cross entropy loss. The suggested system's accuracy is assessed using a variety of performance indicators, including sensitivity, specificity, and F1-score. As demonstrated by the experiment findings, the suggested deep learning based classification system has a high degree of accuracy (94%), making it useful for the diagnosis and treatment of pelvic bone tumors. Our promising results might hasten the use of DL-assisted CT diagnosis for pelvic bone tumors in the future.

Reservoir Computing Based on Oxygen-Vacancy-Mediated X-ray Optical Synaptic Device for Medical CT Bone Diagnosis.

Recognition and judgment of X-ray computed tomography (CT) images play a crucial role in medical diagnosis and disease prevention. However, the storage and calculation of the X-ray imaging system applied in the traditional CT diagnosis is separate, and the pathological judgment is based on doctors' experience, which will affect the timeliness and accuracy of decision-making. In this paper, a simple-structured reservoir computing network (RC) is proposed based on Ga2O3 X-ray optical synaptic devices to recognize medical skeletal CT images with high accuracy. Through oxygen vacancy engineering, Ga2O3 X-ray optical synaptic devices with adjustable photocurrent gain and a persistent photoconductivity effect were obtained. By using the Ga2O3 X-ray optical synaptic device as a reservoir, we constructed an RC network for medical skeletal CT diagnosis and verified its image recognition capability using the MNIST data set with an accuracy of 78.08%. In the elbow skeletal CT image recognition task, the recognition rate is as high as 100%. This work constructs a simple-structured RC network for X-ray image recognition, which is of great significance in applications in medical fields.

A Retrospective Analysis of the Computed Tomography Findings and Diagnosis of 53 Cases of Elastofibroma in the Infrascapular Region.

In this work, we have used histopathology as the gold standard for the diagnosis, calculated the sensitivity and positive predictive value (PPV) of computed tomography (CT), and analyzed the CT and clinical characteristics of pathologically proven elastofibromas. A systematic retrospective analysis was performed on all patients with infrascapular lesions who were treated in the hospital from 2006 to 2018. CT and histopathological examinations were performed for all cases, and the CT sensitivity and PPV for the diagnosis of elastofibroma were calculated. 12 of 53 cases (20 lesions) underwent enhanced CT scan after CT plain scan, and the related clinical and CT features of elastofibromas have been discussed. Of the 54 patients treated during the study, CT diagnosis was consistent with histopathology in 53 cases. One was a false-positive patient. The PPV and sensitivity of the CT in the diagnosis of elastofibroma were 93.3% (95% CI 68.0%-99.8%) and 100%, respectively. The CT values of 12 patients with 20 lesions on plain and enhanced scans were statistically significant (P=0.001). The prevalence of elastofibromas in males and females was statistically significant (P=.000). There was no statistically significant difference in the incidence of left and right elastofibromas (P=0.752). There was no significant difference in the volume of left and right lesions (P=0.209) and the volume of elastofibromas between males and females (P=.474). CT is the most practical tool for the evaluation of elastofibromas in the infrascapular region.

APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF THYROID CANCER WITH ENHANCED COMPUTED TOMOGRAPHY

Thyroid Cancer (TC) is a common malignant tumor, head and neck in the incidence of malignant tumors in the seventh, ranked fourth in the incidence in women. There are several methods to diagnose and recognize TC, such as ultrasonic, computed tomography (CT) and other means. While, it is an important role for CT examination in the diagnosis of TC, because it has the characteristics of objectivity, repeatability, and multi-dimensional imaging, and can clearly understand the scope and spatial characteristics of the lesion. CT has unique advantages in showing lymph nodes and distant metastases, such as coarse-walled or thick-walled ring calcification. The early diagnosis of TC mainly relies on manual labor, which is very inefficient. With the continuous development of information science, the construction of TC diagnosis and recognition models based on artificial intelligence (AI) has gradually become a research hotspot. At present, research on AI-based thyroid screening models mainly focuses on four aspects: first, thyroid region segmentation and image denoising based on image-omics; second, the establishment of a high-precision TC risk prediction model based on multi-omics data; third, screening of biomarkers of TC for clinical diagnosis; fourth, establish the early screening model of TC based on AI. This paper reviews the research status of the AI-based thyroid screening model based on the above four aspects. In addition, this paper also summarizes the main challenges faced by the current AI -based TC recognition and detection model and it proposes a new research idea for the future TC early screening research based on enhanced CT.

Emergency transabdominal preperitoneal (TAPP) repair of a strangulated obturator hernia: A literature review and video vignette.

Obturator hernia (OH), a rare and potentially life-threatening condition, presents diagnostic and therapeutic challenges. This review article comprehensively delves into the clinical features, diagnosis, and management of OH, with a particular emphasis on the pivotal role of computed tomography (CT) in timely and accurate diagnosis. Delays, particularly in contrast-enhanced CT, dramatically increase mortality due to potential bowel strangulation. To illustrate the challenges and complexities surrounding OH, we present a video vignette of a 74-year-old female patient who presented with symptoms suggestive of bowel obstruction (BO) secondary to a strangulated left-sided OH. This patient case complements the theoretical framework established in the review, serving as a practical example for healthcare professionals. Her presentation included abdominal pain, absence of flatus and bowel movements, and abdominal distension. Laboratory tests demonstrated a mildly elevated white blood cell count and C-reactive protein. CT imaging confirmed the diagnosis of a strangulated OH with an ischemic small bowel. An emergency laparoscopy was undertaken, and the hernia was repaired using the transabdominal preperitoneal approach. A portion of the ischemic small bowel was resected through a 5-cm extension of the umbilical port, and an anastomosis was performed using a modified Barcelona technique. The surgery was successfully completed without immediate or long-term complications. This case highlights the crucial role of timely CT diagnosis and minimally invasive surgical management in achieving improved outcomes in acute BO secondary to OH, particularly when facilitated by pre-operative CT planning.

Comparison of the patient demographics, high resolution computed tomography(HRCT) features of the pulmonary ground-glass opacity(GGO) and its diagnostic value analysis

Abstract Introduction Pulmonary ground-glass opacity (GGO) observed on computed tomography (CT) is widely regarded as a diagnostic feature of lung adenocarcinoma. However, a significant radiological predictive sign remains controversial. In this retrospective study, we conducted a comprehensive analysis of 206 patients with GGO to establish a correlation analysis model between CT images and diagnosis of GGO nodules.Method Histopathologic specimens were obtained from 206 patients. The clinical data, pathologic findings, and thin-section CT features of solid, pure GGO (pGGO), and mixed GGO (mGGO) nodules were compared using rigorous statistical methods, such as t-test, Fisher’s exact test, or univariate logistic regression analysis.Results Significant differences were observed among the three groups in terms of patient demographics, including gender, smoking history, and nodule size. Morphologic CT characteristics, such as the presence of spiculated sign, lobulated sign, vascule sign bubble-lucency sign, or pleural retraction, exhibited significant differences among the solid nodule, pGGO, and mGGO groups. However, no significant differences were observed in terms of air-bronchogram sign. Notably, the incidence of malignancy was significantly higher in pGGO nodules (76.52%) than in solid (48.48%) and mGGO (73.86%) nodules. Patients with mGGO had up to 2.988 times higher hazard of malignant lesions than those with solid nodules (p=0.036). Similarly, the hazard of malignant lesions was 2.941 times higher in patients with pGGO than in those with solid nodules (p=0.007).Conclusion Based on our comprehensive analysis, patients presenting with a mGGO or pGGO on CT scans were more likely to be diagnosed with lung cancer.

Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning

AbstractDrowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning diagnosis. However, the validity of the DL models was not assessed, raising doubts about whether the learned features accurately represented the medical findings observed by human experts. In this paper, we assessed the medical validity of DL models that had achieved high classification performance for drowning diagnosis. This retrospective study included autopsy cases aged 8–91 years who underwent postmortem computed tomography between 2012 and 2021 (153 drowning and 160 non-drowning cases). We first trained three deep learning models from a previous work and generated saliency maps that highlight important features in the input. To assess the validity of models, pixel-level annotations were created by four radiological technologists and further quantitatively compared with the saliency maps. All the three models demonstrated high classification performance with areas under the receiver operating characteristic curves of 0.94, 0.97, and 0.98, respectively. On the other hand, the assessment results revealed unexpected inconsistency between annotations and models’ saliency maps. In fact, each model had, respectively, around 30%, 40%, and 80% of irrelevant areas in the saliency maps, suggesting the predictions of the DL models might be unreliable. The result alerts us in the careful assessment of DL tools, even those with high classification performance.

Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT

ObjectivesDetecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients.Materials and methodsLasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR.ResultsA total of 55 patients, including 20 with and 35 without ASR, were included in the radiomic analysis. The best performing model was achieved by Lasso regression tested with the LOOT approach, reaching an area under the curve (AUC) of 0.97 and an accuracy of 92.73%. The XGBoost classifier demonstrated better performance when trained with all extracted radiomic features than without feature selection, achieving an AUC of 0.93 and an accuracy of 89.09%. The diff-maps correctly highlighted post-ablative liver tumor recurrence in all patients.ConclusionsMachine learning-based radiomic analysis and growth visualization proved effective in detecting ablation site recurrence on follow-up CT scans.

Reliability of computed tomography to determine the eligibility of conversion surgery for gastric cancer with para-aortic lymph node metastasis.

259 Background: Although gastric cancer (GC) with para-aortic lymph node (PAN) metastasis is diagnosed as stage IV and basically treated with chemotherapy, it can be the candidate of conversion surgery when PAN metastasis was limited in localized area as oligometastatic status. Recently, we reported that pathological complete response at PAN (pCR at PAN) after chemotherapy was a good surrogate marker in conversion surgery for GC with PAN metastasis. However, it is difficult to predict pCR at PAN before surgery. Computed tomography (CT) is a standard procedure for clinical diagnosis of lymph node metastasis in GC.This study aimed to evaluate the reliability of CT to predict pCR at PAN after chemotherapy. Methods: The study examined patients who received radical gastrectomy with D2 and PAN dissection after chemotherapy for gastric cancer with PAN metastasis (#16a2/b1) from 2004 to 2020. PAN greater than 10mm in long axis was diagnosed as clinically positive. Patients were classified into 2 groups as pPAN-CR and pPAN-nonCR according to the pathological diagnosis of PAN. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for PAN diagnosis were retrospectively investigated. Moreover, the relationship between shrinkage ratio at PAN and pathological diagnosis was also investigated. Results: A total of 46 patients were examined in the present study. The number of pPAN-CR and pPAN-nonCR patients were 31 and 15, respectively. 5-year overall survival rate was 76% in pPAN-CR and 0% in pPAN-nonCR patients. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of PAN were 52.2%, 60.0%, 48.4%, 36.0%, and 28.6%, respectively. Median shrinkage ratio of PAN was 38% (-20%-88%) in pPAN-CR and 40% (-20%-75%) in pPAN-nonCR patients. There was no statistically significant relationship between shrinkage ratio and pathological diagnosis of PAN ( p=0.655). Conclusions: CT diagnosis for PAN after chemotherapy was not reliable. The indication of conversion surgery for GC with PAN metastasis should not be determined based on the size and shrinkage of PAN.

“KAIZEN” method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals

Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use, primarily owing to methodological defects and the lack of implementation considerations for inference. In this study, all development processes of the deep-learning models are performed based on strict criteria of the “KAIZEN checklist”, which is proposed based on previous AI development guidelines to overcome the deficiencies mentioned above. We develop and evaluate two binary-classification deep-learning models to triage COVID-19: a slice model examining a Computed Tomography (CT) slice to find COVID-19 lesions; a series model examining a series of CT images to find an infected patient. We collected 2,400,200 CT slices from twelve emergency centers in Japan. Area Under Curve (AUC) and accuracy were calculated for classification performance. The inference time of the system that includes these two models were measured. For validation data, the slice and series models recognized COVID-19 with AUCs and accuracies of 0.989 and 0.982, 95.9% and 93.0% respectively. For test data, the models’ AUCs and accuracies were 0.958 and 0.953, 90.0% and 91.4% respectively. The average inference time per case was 2.83 s. Our deep-learning system realizes accuracy and inference speed high enough for practical use. The systems have already been implemented in four hospitals and eight are under progression. We released an application software and implementation code for free in a highly usable state to allow its use in Japan and globally.

Tracheobronchial variations in Pneumoconiosis cases: multidetector computed tomography diagnosis.

Tracheobronchial variations (TBVs) are more common than previously believed due to the increasing use of multi-detector computed tomography (MDCT). This study aimed to assess TBVs in cases of pneumoconiosis, one of the oldest occupational diseases that still poses a threat to public health. This was a descriptive study that involved reviewing chest MDCT images of 34 cases of pneumoconiosis and 34 control cases retrospectively from January 2020 to April 2022. Variations in the trachea, right main bronchus, left main bronchus, lobar and segmental branches of the cases in the patient and control groups were evaluated according to Boyden's nomenclature. The frequency of TBV was 32.4% in pneumoconiosis cases. Although the frequency of TBV was higher in the patient group than in the control group, the difference was not statistically significant (p=0.086). Furthermore, there was no significant difference in terms of TBV classification between the patient and control groups (p=0.407). Additionally, the presence of TBV did not affect the distribution of International Labour Organization categories in pneumoconiosis cases (p=0.360). Although our study provides initial insights into the occurrence of TBVs in pneumoconiosis cases, further research is needed to clarify the relationship between these variations and the disease.

DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis

In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.

Multi-slice spiral computed tomography diagnosis of juxta-papillary duodenal diverticulum and its relationship with biliopancreatic diseases.

Juxta-papillary duodenal diverticula (JPDD) are common but are usually asymptomatic, and they are often diagnosed by coincidence. To analyse the anatomy and classification of JPDD and its relationship with biliary and pancreatic disorders, and to explore the diagnostic value of multi-slice spiral computed tomography (MSCT) in patients with JPDD. The imaging data of patients with JPDD, which was obtained via abdominal computed tomography examination and confirmed via gastroscopy and/or upper gastrointestinal barium enema, in our hospital from 1 January 2019 to 31 December 2020 were retrospectively analysed. All patients were scanned using MSCT, and the imaging findings, classification and grading were analysed. A total of 119 duodenal diverticula were detected in 96 patients, including 73 single diverticula and 23 multiple diverticula. The imaging findings were mainly cystic lesions of the inner wall of the duodenum protruding to the outside of the cavity. The thin layer showed a narrow neck connected with the duodenal cavity, and the shape and size of the diverticula were different: 67 central-type cases and 29 peripheral-type cases. There were 50 cases of type I, 33 cases of type II, 19 cases of type III and six cases of type IV. Furthermore, there were seven small, 87 medium and 14 large diverticula. The differences in the location and size of the JPDD in MSCT grading were statistically significant (P< 0.05). The MSCT method has an important diagnostic value for the classification of JPDD, and MSCT images are helpful in the clinical evaluation of patients with JPDD and the selection of treatment options.

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density. A retrospective analysis was conducted on the clinical data of 130 individuals diagnosed with PNs based on pathological confirmation. The utilization of AI and physicians has been employed in the diagnostic process of distinguishing benign and malignant PNs. The CT images depicting PNs were integrated into AI-based software. The gold standard for evaluating the accuracy of AI diagnosis software and physician interpretation was the pathological diagnosis. Out of 226 PNs screened from 130 patients diagnosed by AI and physician reading based on CT, 147 were confirmed by pathology. AI had a sensitivity of 94.69% and radiologists had a sensitivity of 85.40% in identifying PNs. The chi-square analysis indicated that the screening capacity of AI was superior to that of physician reading, with statistical significance (p <0.05). 195 of the 214 PNs suggested by AI were confirmed pathologically as malignant, and 19 were identified as benign; among the 29 PNs suggested by AI as low risk, 13 were confirmed pathologically as malignant, and 16 were identified as benign. From the physician reading, 193 PNs were identified as malignant, 183 were confirmed malignant by pathology, and 10 appeared benign. Physician reading also identified 30 low-risk PNs, 19 of which were pathologically malignant and 11 benign. The physician readings and AI had kappa values of 0.432 and 0.547, respectively. The physician reading and AI area under curves (AUCs) were 0.814 and 0.798, respectively. Both of the diagnostic techniques had worthy diagnostic value, as indicated by their AUCs of >0.7. It is anticipated that the use of AI-based CT diagnosis in the detection of PNs would increase the precision in early detection of lung carcinoma, as well as yield more precise evidence for clinical management.

A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion

Patients with acute coronary syndromes caused by plaque erosion might be managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires an invasive imaging procedure. We sought to develop a deep learning (DL) model that enables an accurate diagnosis of plaque erosion using coronary computed tomography angiography (CTA). A total of 532 CTA scans from 395 patients were used to develop a DL model: 426 CTA scans from 316 patients for training and internal validation, and 106 separate scans from 79 patients for validation. Momentum Distillation-enhanced Composite Transformer Attention (MD-CTA), a novel DL model that can effectively process the entire set of CTA scans to diagnose plaque erosion, was developed. The novel DL model, compared to the convolution neural network, showed significantly improved AUC (0.899 [0.841–0.957] vs. 0.724 [0.622–0.826]), sensitivity (87.1 [70.2–96.4] vs. 71.0 [52.0–85.8]), and specificity (85.3 [75.3–92.4] vs. 68.0 [56.2–78.3]), respectively, for the patient-level prediction. Similar results were obtained at the slice-level prediction AUC (0.897 [0.890–0.904] vs. 0.757 [0.744–0.770]), sensitivity (82.2 [79.8–84.3] vs. 68.9 [66.2–71.6]), and specificity (80.1 [79.1–81.0] vs. 67.3 [66.3–68.4]), respectively. This newly developed DL model enables an accurate CT diagnosis of plaque erosion, which might enable cardiologists to provide tailored therapy without invasive procedures.Clinical Trial Registration:http://www.clinicaltrials.gov, NCT04523194.

CT Findings of Stage IA Ovarian Cancer: Comparison between Borderline Tumor and Stage IC Ovarian Cancer.

Ovarian cancer is a common gynecological malignant tumor in women. Most patients have reached the advanced stage when they visit the hospital. In order to diagnose ovarian cancer at an early stage, treat it at an early stage, and improve the survival rate of patients, this study has used the imaging computed tomography (CT) method to diagnose stage IA ovarian cancer. The purpose of this work was to study CT features of stage IA ovarian cancer, and compare the borderline tumor and stage IC ovarian cancer at the same time so as to improve the CT diagnosis of early ovarian cancer. We retrospectively collected clinical and CT data of patients with stage I ovarian cancer and borderline ovarian tumor admitted to Nantong Tumor Hospital from 2013 to 2021. Altogether, 23 cases of patients (borderline ovarian tumor, 9 cases; stage IA ovarian cancer, 5 cases; stage IC ovarian cancer, 9 cases) were involved. CT characteristics of these patients were analyzed in terms of the tumor diameter, cystic solid structure, solid component, septation, enhancement, peritoneal thickening, ascites, and abdominal lymph nodes. CT features of stage IA ovarian cancer included large tumor size (average diameter: 15 cm), cystic solid structure (4/5; 80%), septation (4/5; 80%), and enhanced cystic wall, septum, or solid components of the tumor on contrast-enhanced CT (5/5; 100%) no peritoneal thickening (0/5; 0%), no ascites (0/5; 0%), and no abdominal lymph node enlargement (0/5; 0%). The tumor structure did not differ significantly between stage IA and IC ovarian cancers (p > 0.05), while intraperitoneal ascites did (χ2 = 0.031; p < 0.05). Stage IA and borderline ovarian tumors did not differ significantly in ovarian tumor structure (p > 0.05). CT features of stage IA ovarian cancer included large tumor size, cystic solid structure, septation, and enhanced cystic wall and solid parts in the tumors. No pelvic or abdominal metastasis was observed.

An Intracranial Hemorrhage Complication: Terson Syndrome

Abstract Vitreous hemorrhage associated with subarachnoid hemorrhage is known as Terson's Syndrome (TS). However, it is also seen in the literature in cases of traumatic brain injury or intracranial hemorrhage. Management of visual manifestations is necessary in patients with intracranial hemorrhage. A vitreous hemorrhage was found in the evaluation made due to visual symptoms in a patient who presented to the emergency department with intracranial hemorrhage. In addition to clinical findings, imaging techniques have an important place in the diagnosis of TS. Vitreous hemorrhage findings can be detected with imaging studies such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In this case, the diagnostic findings and the importance of diagnosis in CT and MRI examinations in a patient with TS will be discussed. The purpose of this case report is to share the imaging findings of TS.

Computerized tomography diagnosis of pulmonary fungal infection in patients with acute leukemia after chemotherapy

Computerized tomography diagnosis of pulmonary fungal infection in patients with acute leukemia after chemotherapy

Diagnosis and Surgical Treatment of a Gunshot Fragment Wound of the Heart with a Threat of Coronary Artery Perforation

Each case of fragment heart damage is unique and requires careful preparation for surgical intervention. Complexity of such operations is explained by the probability of unwanted complications associated with technical measures and aimed at extracting fragments of the most diverse localization.&#x0D; The aim. To demonstrate the possibilities of effective diagnosis and optimal surgical treatment of a gunshot fragment wound of the heart with the threat of coronary artery perforation.&#x0D; Material and methods. The article presents a case of diagnosis and surgical treatment of a fragment heart damage with intramyocardial damage of the left ventricular wall with localization under the circumflex branch of the left coronary artery with the threat of its perforation. One day has passed since a mine blast injury. Diagnosis was based on the use of contrast enhanced computed tomography (CT) and coronary angiography.&#x0D; Results. During the diagnostic process, the need to use a CT scan with mandatory contrast media arose, which made it possible to confirm the presence of a fragment in the immediate vicinity of the coronary artery. Coronarography provided additional information which revealed contact of the fragment with the coronary artery with the threat of its perforation. Immediate surgery with artificial blood circulation with stepwise application of magnets of increasing power allowed safe removal of the fragment, avoiding damage to the coronary artery.&#x0D; Conclusions. The most accurate topical diagnosis of fragment localization in heart damage requires CT diagnosis with mandatory contrast media and, if possible, with 3D reconstruction, which allows for surgical intervention with a minimal risk of intraoperative complications. Location of the fragments near the coronary vessels may threaten their damaging during removal and requires careful manipulation to prevent injury to the heart vessels. Detection and removal of sharp fragments located near the coronary vessels require stepwise application of magnets of increasing power to bring the fragment to a safe zone with its subsequent extraction.

Comparison of cone-beam and fan-beam computed tomography and low-field magnetic resonance imaging for detection of proximal phalanx dorsoproximal osteochondral defects.

Dorsoproximal osteochondral defects commonly affect the proximal phalanx, but information about diagnosis on computed tomography (CT) and magnetic resonance imaging (MRI) is limited. To assess CT and MRI diagnoses of osteochondral defects, describe the lesions and compare sensitivity and specificity of the modalities using macroscopic pathology as gold standard. Cross-sectional study. Thirty-five equine cadaver limbs underwent standing cone-beam CT (CBCT), fan-beam CT (FBCT), low-field MRI and pathological examination. CT and MR images were examined for proximal phalanx dorsomedial and dorsolateral eminence osteochondral defects. Defect dimensions were measured. Imaging diagnoses and measurements were compared with macroscopic examination. Fifty-six defects were seen over 70 potential locations. On CBCT and FBCT, osteochondral defects appeared as subchondral irregularity/saucer-shaped defects. On MRI, osteochondral defects were a combination of articular cartilage defect on dorsal images and subchondral flattening/irregularity on sagittal images. Subchondral thickening and osseous short tau inversion recovery hyperintensity were found concurrent with osteochondral defects. Compared with pathological examination, the sensitivity and specificity of diagnosis were 86% (95% confidence interval [95% CI] 75%-93%) and 64% (95% CI 38%-85%) for FBCT; 64% (95% CI 51%-76%) and 71% (95% CI 46%-90%) for CBCT; and 52% (95% CI 39%-65%) and 71% (95% CI 46%-90%) for MRI. Sensitivity of all modalities increased with defect size. Macroscopic defect dimensions were strongly correlated with CBCT (r = 0.76, p < 0.001) and moderately correlated with FBCT and MRI (r = 0.65, p < 0.001). Macroscopic measurements were significantly greater than all imaging modality dimensions (p < 0.001), potentially because macroscopy included articular cartilage pathology. Influence of motion artefact could not be assessed. Osteochondral defects could be visualised using both CT and MRI with sensitivity increasing with defect size. Diagnostic performance was best using FBCT, followed by CBCT then MRI, but CBCT-measured defect size best correlated with macroscopic examination. MRI provided useful information on fluid signal associated with defects, which could represent active pathology.