Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning.

BACKGROUND: Radiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age assessment based on clavicle ossification in computed tomography (CT). METHODS: Thoracic CT scans were retrospectively collected from the picture archiving and communication system. Individuals aged 15.0 to 30.0 years examined in routine clinical practice were included. All scans were automatically cropped around the medial clavicular epiphyseal cartilages. A deep learning model was trained to predict a person's chronological age based on these scans. Performance was evaluated using mean absolute error (MAE). Model performance was compared to an optimistic human reader performance estimate for an established reference study method. RESULTS: The deep learning model was trained on 4,400 scans of 1,935 patients (training set: mean age = 24.2 years ± 4.0, 1132 female) and evaluated on 300 scans of 300 patients with a balanced age and sex distribution (test set: mean age = 22.5 years ± 4.4, 150 female). Model MAE was 1.65 years, and the highest absolute error was 6.40 years for females and 7.32 years for males. However, performance could be attributed to norm-variants or pathologic disorders. Human reader estimate MAE was 1.84 years and the highest absolute error was 3.40 years for females and 3.78 years for males. CONCLUSIONS: We present a deep learning approach for continuous age predictions using CT volumes highlighting the medial clavicular epiphyseal cartilage with performance comparable to the human reader estimate.

ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports.

OBJECTIVES: To assess the quality of simplified radiology reports generated with the large language model (LLM) ChatGPT and to discuss challenges and chances of ChatGPT-like LLMs for medical text simplification. METHODS: In this exploratory case study, a radiologist created three fictitious radiology reports which we simplified by prompting ChatGPT with "Explain this medical report to a child using simple language." In a questionnaire, we tasked 15 radiologists to rate the quality of the simplified radiology reports with respect to their factual correctness, completeness, and potential harm for patients. We used Likert scale analysis and inductive free-text categorization to assess the quality of the simplified reports. RESULTS: Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed relevant medical information, and potentially harmful passages were reported. CONCLUSION: While we see a need for further adaption to the medical field, the initial insights of this study indicate a tremendous potential in using LLMs like ChatGPT to improve patient-centered care in radiology and other medical domains. CLINICAL RELEVANCE STATEMENT: Patients have started to use ChatGPT to simplify and explain their medical reports, which is expected to affect patient-doctor interaction. This phenomenon raises several opportunities and challenges for clinical routine. KEY POINTS: • Patients have started to use ChatGPT to simplify their medical reports, but their quality was unknown. • In a questionnaire, most participating radiologists overall asserted good quality to radiology reports simplified with ChatGPT. However, they also highlighted a notable presence of errors, potentially leading patients to draw harmful conclusions. • Large language models such as ChatGPT have vast potential to enhance patient-centered care in radiology and other medical domains. To realize this potential while minimizing harm, they need supervision by medical experts and adaption to the medical field.

Automated localization of the medial clavicular epiphyseal cartilages using an object detection network: a step towards deep learning-based forensic age assessment.

BACKGROUND: Deep learning is a promising technique to improve radiological age assessment. However, expensive manual annotation by experts poses a bottleneck for creating large datasets to appropriately train deep neural networks. We propose an object detection approach to automatically annotate the medial clavicular epiphyseal cartilages in computed tomography (CT) scans. METHODS: The sternoclavicular joints were selected as structure-of-interest (SOI) in chest CT scans and served as an easy-to-identify proxy for the actual medial clavicular epiphyseal cartilages. CT slices containing the SOI were manually annotated with bounding boxes around the SOI. All slices in the training set were used to train the object detection network RetinaNet. Afterwards, the network was applied individually to all slices of the test scans for SOI detection. Bounding box and slice position of the detection with the highest classification score were used as the location estimate for the medial clavicular epiphyseal cartilages inside the CT scan. RESULTS: From 100 CT scans of 82 patients, 29,656 slices were used for training and 30,846 slices from 110 CT scans of 110 different patients for testing the object detection network. The location estimate from the deep learning approach for the SOI was in a correct slice in 97/110 (88%), misplaced by one slice in 5/110 (5%), and missing in 8/110 (7%) test scans. No estimate was misplaced by more than one slice. CONCLUSIONS: We demonstrated a robust automated approach for annotating the medial clavicular epiphyseal cartilages. This enables training and testing of deep neural networks for age assessment.

How to exclude pulmonary embolism in patients hospitalized with COVID-19: a comparison of predictive scores.

BACKGROUND: Pulmonary embolism (PE) is an important complication of Coronavirus disease 2019 (COVID-19). COVID-19 is associated with respiratory impairment and a pro-coagulative state, rendering PE more likely and difficult to recognize. Several decision algorithms relying on clinical features and D-dimer have been established. High prevalence of PE and elevated Ddimer in patients with COVID-19 might impair the performance of common decision algorithms. Here, we aimed to validate and compare five common decision algorithms implementing age adjusted Ddimer, the GENEVA, and Wells scores as well as the PEGeD- and YEARS-algorithms in patients hospitalized with COVID-19. METHODS: In this single center study, we included patients who were admitted to our tertiary care hospital in the COVID-19 Registry of the LMU Munich. We retrospectively selected patients who received a computed tomography pulmonary angiogram (CTPA) or pulmonary ventilation/perfusion scintigraphy (V/Q) for suspected PE. The performances of five commonly used diagnostic algorithms (age-adjusted D-dimer, GENEVA score, PEGeD-algorithm, Wells score, and YEARS-algorithm) were compared. RESULTS: We identified 413 patients with suspected PE who received a CTPA or V/Q confirming 62 PEs (15%). Among them, 358 patients with 48 PEs (13%) could be evaluated for performance of all algorithms. Patients with PE were older and their overall outcome was worse compared to patients without PE. Of the above five diagnostic algorithms, the PEGeD- and YEARS-algorithms performed best, reducing diagnostic imaging by 14% and 15% respectively with a sensitivity of 95.7% and 95.6%. The GENEVA score was able to reduce CTPA or V/Q by 32.2% but suffered from a low sensitivity (78.6%). Age-adjusted D-dimer and Wells score could not significantly reduce diagnostic imaging. CONCLUSION: The PEGeD- and YEARS-algorithms outperformed other tested decision algorithms and worked well in patients admitted with COVID-19. These findings need independent validation in a prospective study.

Lung Ultrasound as a Promising Diagnostic Tool for Primary Graft Dysfunction after Lung Transplantation.

PURPOSE: The aim of the study was to evaluate whether the quantification of B-lines via lung ultrasound after lung transplantation is feasible and correlates with the diagnosis of primary graft dysfunction. METHODS: Following lung transplantation, patients underwent daily lung ultrasound on postoperative days 1-3. B-lines were quantified by an ultrasound score based on the number of single and confluent B-lines per intercostal space, using a four-region protocol. The ultrasound score was correlated with the diagnosis of primary graft dysfunction. Furthermore, correlation analyses and receiver operating characteristics analyses taking into account ultrasound score, chest radiographs, and PaO2/FiO2 ratio were performed. RESULTS: A total of 32 patients (91 ultrasound measurements) were included, whereby 10 were diagnosed with primary graft dysfunction. The median B-line score was 5 [IQR: 4, 8]. There was a significant correlation between B-line score and the diagnosis of primary graft dysfunction (r = 0.59, p < 0.001). A significant correlation could also be seen between chest X-rays and primary graft dysfunction (r = 0.34, p = 0.008), but the B-line score showed superiority over chest X-rays with respect to diagnosing primary graft dysfunction in the receiver operating characteristics curves with an area under the curve value of 0.921 versus 0.708. There was a significant negative correlation between B-line score and PaO2/FiO2 ratio (r = -0.41, p < 0.001), but not between chest X-rays and PaO2/FiO2 ratio (r = -0.14, p = 0.279). CONCLUSION: The appearance of B-lines correlated well with primary graft dysfunction and outperformed chest radiographs.

Pneumothorax detection in chest radiographs: optimizing artificial intelligence system for accuracy and confounding bias reduction using in-image annotations in algorithm training.

OBJECTIVES: Diagnostic accuracy of artificial intelligence (AI) pneumothorax (PTX) detection in chest radiographs (CXR) is limited by the noisy annotation quality of public training data and confounding thoracic tubes (TT). We hypothesize that in-image annotations of the dehiscent visceral pleura for algorithm training boosts algorithm's performance and suppresses confounders. METHODS: Our single-center evaluation cohort of 3062 supine CXRs includes 760 PTX-positive cases with radiological annotations of PTX size and inserted TTs. Three step-by-step improved algorithms (differing in algorithm architecture, training data from public datasets/clinical sites, and in-image annotations included in algorithm training) were characterized by area under the receiver operating characteristics (AUROC) in detailed subgroup analyses and referenced to the well-established "CheXNet" algorithm. RESULTS: Performances of established algorithms exclusively trained on publicly available data without in-image annotations are limited to AUROCs of 0.778 and strongly biased towards TTs that can completely eliminate algorithm's discriminative power in individual subgroups. Contrarily, our final "algorithm 2" which was trained on a lower number of images but additionally with in-image annotations of the dehiscent pleura achieved an overall AUROC of 0.877 for unilateral PTX detection with a significantly reduced TT-related confounding bias. CONCLUSIONS: We demonstrated strong limitations of an established PTX-detecting AI algorithm that can be significantly reduced by designing an AI system capable of learning to both classify and localize PTX. Our results are aimed at drawing attention to the necessity of high-quality in-image localization in training data to reduce the risks of unintentionally biasing the training process of pathology-detecting AI algorithms. KEY POINTS: • Established pneumothorax-detecting artificial intelligence algorithms trained on public training data are strongly limited and biased by confounding thoracic tubes. • We used high-quality in-image annotated training data to effectively boost algorithm performance and suppress the impact of confounding thoracic tubes. • Based on our results, we hypothesize that even hidden confounders might be effectively addressed by in-image annotations of pathology-related image features.

Evaluation of patients with respiratory infections during the first pandemic wave in Germany: characteristics of COVID-19 versus non-COVID-19 patients.

BACKGROUND: Characteristics of COVID-19 patients have mainly been reported within confirmed COVID-19 cohorts. By analyzing patients with respiratory infections in the emergency department during the first pandemic wave, we aim to assess differences in the characteristics of COVID-19 vs. Non-COVID-19 patients. This is particularly important regarding the second COVID-19 wave and the approaching influenza season. METHODS: We prospectively included 219 patients with suspected COVID-19 who received radiological imaging and RT-PCR for SARS-CoV-2. Demographic, clinical and laboratory parameters as well as RT-PCR results were used for subgroup analysis. Imaging data were reassessed using the following scoring system: 0 - not typical, 1 - possible, 2 - highly suspicious for COVID-19. RESULTS: COVID-19 was diagnosed in 72 (32,9%) patients. In three of them (4,2%) the initial RT-PCR was negative while initial CT scan revealed pneumonic findings. 111 (50,7%) patients, 61 of them (55,0%) COVID-19 positive, had evidence of pneumonia. Patients with COVID-19 pneumonia showed higher body temperature (37,7 ± 0,1 vs. 37,1 ± 0,1 °C; p = 0.0001) and LDH values (386,3 ± 27,1 vs. 310,4 ± 17,5 U/l; p = 0.012) as well as lower leukocytes (7,6 ± 0,5 vs. 10,1 ± 0,6G/l; p = 0.0003) than patients with other pneumonia. Among abnormal CT findings in COVID-19 patients, 57 (93,4%) were evaluated as highly suspicious or possible for COVID-19. In patients with negative RT-PCR and pneumonia, another third was evaluated as highly suspicious or possible for COVID-19 (14 out of 50; 28,0%). The sensitivity in the detection of patients requiring isolation was higher with initial chest CT than with initial RT-PCR (90,4% vs. 79,5%). CONCLUSIONS: COVID-19 patients show typical clinical, laboratory and imaging parameters which enable a sensitive detection of patients who demand isolation measures due to COVID-19.

Macrophage ablation significantly reduces uptake of imaging probe into organs of the reticuloendothelial system.

BACKGROUND: Macrophages engulf particulate contrast media, which is pivotal for biomedical imaging. PURPOSE: To introduce a macrophage ablation animal model by showing its power to manipulate the kinetics of imaging probes. MATERIAL AND METHODS: The kinetics of a particulate computed tomography (CT) contrast media was compared in macrophage ablative mice and normal mice. Liposomes (size 220 µg), loaded with clodronate, were injected into the peritoneum of three C57BL/6 mice. On the third day, 200 µL of the particulate agent ExiTron nano 6000 were injected into three macrophage-ablative mice and three control mice. CT scans were acquired before and 3 min, 1 h, 6 h, and 24 h after the ExiTron application. The animals were sacrificed, and their spleens and livers removed. Relative CT values (CTV) were measured and analyzed. RESULTS: Liver and spleen enhancement of treated mice and controls were increasing over time. The median peak values were different with 225 CTV for treated mice and 582 CTV for controls in the liver (P = 0.032) and 431 CTV for treated and 974 CTV in controls in the spleen (P = 0.016). CONCLUSION: Macrophage ablation leads to a decrease of enhancement in organs containing high numbers of macrophages, but only marginal changes in macrophage-poor organs. Macrophage ablation can influence the phagocytic activity and thus opens new potentials to investigate and manipulate the uptake of imaging probes.

Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

OBJECTIVES: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies. DESIGN: Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients. PATIENTS: One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between. MEASUREMENTS AND MAIN RESULTS: Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78-0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist's maximum sensitivity up to 0.87 (95% CI, 0.76-0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points. CONCLUSIONS: Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence-assisted reading setting.

Contrast-Enhanced Ultrasound (CEUS) for the Evaluation of Bosniak III Complex Renal Cystic Lesions-A 10-Year Specialized European Single-Center Experience with Histopathological Validation.

Background and objectives: The aim of the present retrospective single-center study is to evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) for assessing Bosniak III complex renal cystic lesions with histopathological validation. Materials and Methods: 49 patients with CEUS-categorized Bosniak III renal cystic lesions were included in this retrospective study. All patients underwent native B-mode, Color Doppler, contrast-enhanced ultrasound (CEUS) between 2010-2020. Eight and five patients underwent computed tomography (CT) and magnetic resonance imaging (MRI), respectively. Twenty-nine underwent (partial) nephrectomy allowing for histopathological analysis. The applied contrast agent for CEUS was a second-generation blood pool agent. Ultrasonography examinations were performed and interpreted by a single experienced radiologist with more than 15 years of experience (EFSUMB Level 3). Results: CEUS examinations were successfully performed in all included patients without registering any adverse effects. The malignancy rate of CEUS-categorized Bosniak III renal lesions accounted for 66%. Initially, cystic complexity was visualized in native B-mode. In none of the renal lesions hypervascularization was detected in Color Doppler. CEUS allowed for detection of contrast enhancement patterns in all included Bosniak III renal lesions. Delayed wash-out could be detected in 6/29 renal lesions. In two cases of histopathologically confirmed clear-cell RCC, appropriate up-grading from Bosniak IIF to III was achieved by CEUS. Conclusions: CEUS depicts a promising imaging modality for the precise diagnostic workup and stratification of renal cystic lesions according to the Bosniak classification system, thereby helping guidance of adequate clinical management in the future.

Cost-effectiveness of Endovascular Therapy for Acute Ischemic Stroke: A Systematic Review of the Impact of Patient Age.

Purpose To determine the impact of patient age on the cost-effectiveness of endovascular therapy (EVT) in addition to standard care (SC) in large-vessel-occlusion stroke for patients aged 50 to 100 years in the United States. Materials and Methods A decision-analytic Markov model was used to estimate direct and indirect lifetime costs and quality-adjusted life years (QALYs). Age-dependent input parameters were obtained from the literature. Deterministic and probabilistic sensitivity analysis for age at index stroke were used. The willingness-to-pay (WTP) was set to thresholds of $50 000, $100 000, and $150 000 per QALY. The study applied a U.S. setting for health care and societal perspectives. Incremental costs and effectiveness were derived from deterministic and probabilistic sensitivity analysis. Acceptability rates at different WTP thresholds were determined. Results EVT+SC was the dominant strategy in patients aged 50 to 79 years. The highest incremental effectiveness (2.61 QALYs) and cost-savings (health care perspective, $99 555; societal perspective, $146 385) were obtained in 50-year-old patients. In octogenarians (80-89 years), EVT+SC led to incremental QALYs at incremental costs with acceptability rates of more than 85%, more than 99%, and more than 99% at a WTP of $50 000, $100 000, and $150 000 per QALY, respectively. In nonagenarians (90-99 years), acceptability rates at a WTP of $50 000 per QALY dropped but stayed higher than 85% and higher than 95% at thresholds of $100 000 and $150 000 per QALY. Conclusion Using contemporary willingness-to-pay thresholds in the United States, endovascular therapy in addition to standard care reduces lifetime costs for patients up to 79 years of age and is cost-effective for patients aged 80 to 100 years.

Structured reports of videofluoroscopic swallowing studies have the potential to improve overall report quality compared to free text reports.

PURPOSE: To compare free text (FTR) and structured reports (SR) of videofluoroscopic swallowing studies (VFSS) and evaluate satisfaction of referring otolaryngologists and speech therapists. MATERIALS AND METHODS: Both standard FTR and SR of 26 patients with VFSS were acquired. A dedicated template focusing on oropharyngeal phases was created for SR using online software with clickable decision-trees and concomitant generation of semantically structured reports. All reports were evaluated regarding overall quality and content, information extraction and clinical decision support (10-point Likert scale (0 = I completely disagree, 10 = I completely agree)). RESULTS: Two otorhinolaryngologists and two speech therapists evaluated FTR and SR. SR received better ratings than FTR in all items. SR were perceived to contain more details on the swallowing phases (median rating: 10 vs. 5; P < 0.001), penetration and aspiration (10 vs. 5; P < 0.001) and facilitated information extraction compared to FTR (10 vs. 4; P < 0.001). Overall quality was rated significantly higher in SR than FTR (P < 0.001). CONCLUSION: SR of VFSS provide more detailed information and facilitate information extraction. SR better assist in clinical decision-making, might enhance the quality of the report and, thus, are recommended for the evaluation of VFSS. KEY POINTS: • Structured reports on videofluoroscopic exams of deglutition lead to improved report quality. • Information extraction is facilitated when using structured reports based on decision trees. • Template-based reports add more value to clinical decision-making than free text reports. • Structured reports receive better ratings by speech therapists and otolaryngologists. • Structured reports on videofluoroscopic exams may improve the comparability between exams.

Structured Reporting of CT Angiography Runoff Examinations of the Lower Extremities.

OBJECTIVES: The aim was to evaluate the effect of structured reporting of computed tomography angiography (CTA) runoff studies on clarity, completeness, clinical relevance, usefulness of the radiology reports, further testing, and therapy in patients with known or suspected peripheral arterial disease. METHODS: Conventional reports (CRs) and structured reports (SRs) were generated for 52 patients who had been examined with a CTA runoff examination of the lower extremities. The sample size was based on power calculations with a power of 95% and a significance level of .007 (adjusted for multiple testing). CRs were dictated in a free text form; SRs contained a consistent ordering of observations with standardised subheadings. CRs were compared with SRs. Two vascular medicine specialists and two vascular surgeons rated the reports regarding their satisfaction with clarity, completeness, clinical relevance, and usefulness as well as overall satisfaction. Additionally, they made hypothetical decisions on further testing and therapy. Median ratings were compared using the Wilcoxon signed rank test and generalised linear mixed effects models. RESULTS: SRs received higher ratings for satisfaction with clarity (median rating 9.0 vs. 7.0, p < .0001) and completeness (median rating 9.0 vs. 7.5, p < .0001) and were judged to be of greater clinical relevance (median rating 9.0 vs. 8.0, p < .0001) and usefulness (median rating 9.0 vs. 8.0, p < .0001). Overall satisfaction was also higher for SRs (median rating 9.0 vs. 7.0, p < .0001) than CRs. There were no significant differences in further testing or therapy. CONCLUSION: Referring clinicians perceive SRs of CTA runoff examinations of the lower extremities as offering superior clarity, completeness, clinical relevance, and usefulness than CRs. Structured reporting does not appear to alter further testing or therapy in patients with known or suspected peripheral arterial disease.

Structured reporting of x-rays for atraumatic shoulder pain: advantages over free text?

BACKGROUND: To analyse structured and free text reports of shoulder X-ray examinations evaluating the quality of reports and potential contributions to clinical decision-making. METHODS: We acquired both standard free text and structured reports of 31 patients with a painful shoulder without history of previous trauma who received X-ray exams. A template was created for the structured report based on the template ID 0000154 (Shoulder X-ray) from radreport.org using online software with clickable decision trees with concomitant generation of structured semantic reports. All reports were evaluated regarding overall quality and key features: content, information extraction and clinical relevance. RESULTS: Two experienced orthopaedic surgeons reviewed and rated structured and free text reports of 31 patients independently. The structured reports achieved significantly higher median ratings in all key features evaluated (P < 0.001), including facilitation of information extraction (P < 0.001) and better contribution to subsequent clinical decision-making (P < 0.001). The overall quality of structured reports was significantly higher than in free text report (P < 0.001). CONCLUSIONS: A comprehensive structured template may be a useful tool to assist in clinical decision-making and is, thus, recommended for the reporting of degenerative changes regarding X-ray examinations of the shoulder.

Intracranial arterial calcifications as a prognostic factor for subsequent major adverse cardiovascular events (MACE).

BACKGROUND: Intracranial arterial calcifications (ICAC) are often detected on unenhanced CT of patients with an age > 60. However, association with the subsequent occurrence of major adverse cardiovascular events (MACE) has not yet been evaluated. PURPOSE: This study aimed at evaluating the association of ICAC with subsequent MACE and overall mortality. METHODS: In this retrospective, IRB approved study, we included 175 consecutive patients (89 males, mean age 78.3 ± 8.5 years) of age > 60 years who underwent an unenhanced CT of the head due to minor trauma or neurological disorders. Presence of ICAC was determined in seven intracranial arteries using a semi-quantitative scale, which resulted in the calcified plaque score (CPS). Clinical follow-up information was obtained by questionnaires and telephone interviews. MACE was defined as myocardial infarction or revascularization, stroke or death due to cardiovascular event. RESULTS: Mean follow-up time was 39.8 ± 7.8 months, resulting in 579.7 patient-years of follow-up. Overall, 36 MACE occurred during follow-up (annual event rate = 6.2%/year). Mean CPS was significantly higher in subjects with MACE during follow-up compared to subjects without MACE (p < 0.01). In 15 patients CPS was 0; in none of these patients MACE was registered. Kaplan-Meier-analysis revealed that patients with a low plaque burden (CPS < 5) had a significant longer MACE-free and overall survival than patients with a high plaque burden (CPS ≥ 5) (p < 0.01). CONCLUSION: Patients with ICAC have an increased risk for future cardio- or cerebrovascular events. Therefore, ICAC might be a prognostic factor to determine the risk for these events in older patients.

Early Imaging Prediction of Malignant Cerebellar Edema Development in Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Malignant cerebellar edema (MCE) is a life-threatening complication of acute ischemic stroke that requires timely diagnosis and management. Aim of this study was to identify imaging predictors in initial multiparametric computed tomography (CT), including whole-brain CT perfusion (WB-CTP). METHODS: We consecutively selected all subjects with cerebellar ischemic WB-CTP deficits and follow-up-confirmed cerebellar infarction from an initial cohort of 2635 patients who had undergone multiparametric CT because of suspected stroke. Follow-up imaging was assessed for the presence of MCE, measured using an established 10-point scale, of which scores ≥4 are considered malignant. Posterior circulation-Acute Stroke Prognosis Early CT Score (pc-ASPECTS) was determined to assess ischemic changes on noncontrast CT, CT angiography (CTA), and parametric WB-CTP maps (cerebellar blood flow [CBF]; cerebellar blood volume; mean transit time; time to drain). Fisher's exact tests, Mann-Whitney U tests, and receiver operating characteristics analyses were performed for statistical analyses. RESULTS: Out of a total of 51 patients who matched the inclusion criteria, 42 patients (82.4%) were categorized as MCE- and 9 (17.6%) as MCE+. MCE+ patients had larger CBF, cerebellar blood volume, mean transit time, and time to drain deficit volumes (all with P<0.001) and showed significantly lower median pc-ASPECTS assessed using WB-CTP (CBF, cerebellar blood volume, mean transit time, time to drain; all with P<0.001) compared with MCE- patients, while median pc-ASPECTS on noncontrast CT and CTA was not significantly different (both P>0.05). Receiver operating characteristics analyses yielded the largest area under the curve values for the prediction of MCE development for CBF (0.979) and cerebellar blood volume deficit volumes (0.956) and pc-ASPECTS on CBF (0.935), whereas pc-ASPECTS on noncontrast CT (0.648) and CTA (0.684) had less diagnostic value. The optimal cutoff value for CBF deficit volume was 22 mL, yielding 100% sensitivity and 90% specificity for MCE classification. CONCLUSIONS: WB-CTP provides added diagnostic value for the early identification of patients at risk for MCE development in acute cerebellar stroke.

Out-of-distribution detection with in-distribution voting using the medical example of chest x-ray classification.

BACKGROUND: Deep learning models are being applied to more and more use cases with astonishing success stories, but how do they perform in the real world? Models are typically tested on specific cleaned data sets, but when deployed in the real world, the model will encounter unexpected, out-of-distribution (OOD) data. PURPOSE: To investigate the impact of OOD radiographs on existing chest x-ray classification models and to increase their robustness against OOD data. METHODS: The study employed the commonly used chest x-ray classification model, CheXnet, trained on the chest x-ray 14 data set, and tested its robustness against OOD data using three public radiography data sets: IRMA, Bone Age, and MURA, and the ImageNet data set. To detect OOD data for multi-label classification, we proposed in-distribution voting (IDV). The OOD detection performance is measured across data sets using the area under the receiver operating characteristic curve (AUC) analysis and compared with Mahalanobis-based OOD detection, MaxLogit, MaxEnergy, self-supervised OOD detection (SS OOD), and CutMix. RESULTS: Without additional OOD detection, the chest x-ray classifier failed to discard any OOD images, with an AUC of 0.5. The proposed IDV approach trained on ID (chest x-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0.999 OOD AUC across the three data sets, surpassing all other OOD detection methods. Mahalanobis-based OOD detection achieved an average OOD detection AUC of 0.982. IDV trained solely with a few thousand ImageNet images had an AUC 0.913, which was considerably higher than MaxLogit (0.726), MaxEnergy (0.724), SS OOD (0.476), and CutMix (0.376). CONCLUSIONS: The performance of all tested OOD detection methods did not translate well to radiography data sets, except Mahalanobis-based OOD detection and the proposed IDV method. Consequently, training solely on ID data led to incorrect classification of OOD images as ID, resulting in increased false positive rates. IDV substantially improved the model's ID classification performance, even when trained with data that will not occur in the intended use case or test set (ImageNet), without additional inference overhead or performance decrease in the target classification. The corresponding code is available at https://gitlab.lrz.de/IP/a-knee-cannot-have-lung-disease.

German CheXpert Chest X-ray Radiology Report Labeler.

PURPOSE: The aim of this study was to develop an algorithm to automatically extract annotations from German thoracic radiology reports to train deep learning-based chest X-ray classification models. MATERIALS AND METHODS: An automatic label extraction model for German thoracic radiology reports was designed based on the CheXpert architecture. The algorithm can extract labels for twelve common chest pathologies, the presence of support devices, and "no finding". For iterative improvements and to generate a ground truth, a web-based multi-reader annotation interface was created. With the proposed annotation interface, a radiologist annotated 1086 retrospectively collected radiology reports from 2020-2021 (data set 1). The effect of automatically extracted labels on chest radiograph classification performance was evaluated on an additional, in-house pneumothorax data set (data set 2), containing 6434 chest radiographs with corresponding reports, by comparing a DenseNet-121 model trained on extracted labels from the associated reports, image-based pneumothorax labels, and publicly available data, respectively. RESULTS: Comparing automated to manual labeling on data set 1: "mention extraction" class-wise F1 scores ranged from 0.8 to 0.995, the "negation detection" F1 scores from 0.624 to 0.981, and F1 scores for "uncertainty detection" from 0.353 to 0.725. Extracted pneumothorax labels on data set 2 had a sensitivity of 0.997 [95 % CI: 0.994, 0.999] and specificity of 0.991 [95 % CI: 0.988, 0.994]. The model trained on publicly available data achieved an area under the receiver operating curve (AUC) for pneumothorax classification of 0.728 [95 % CI: 0.694, 0.760], while the models trained on automatically extracted labels and on manual annotations achieved values of 0.858 [95 % CI: 0.832, 0.882] and 0.934 [95 % CI: 0.918, 0.949], respectively. CONCLUSION: Automatic label extraction from German thoracic radiology reports is a promising substitute for manual labeling. By reducing the time required for data annotation, larger training data sets can be created, resulting in improved overall modeling performance. Our results demonstrated that a pneumothorax classifier trained on automatically extracted labels strongly outperformed the model trained on publicly available data, without the need for additional annotation time and performed competitively compared to manually labeled data. KEY POINTS: · An algorithm for automatic German thoracic radiology report annotation was developed.. · Automatic label extraction is a promising substitute for manual labeling.. · The classifier trained on extracted labels outperformed the model trained on publicly available data..

Language model-based labeling of German thoracic radiology reports.

The aim of this study was to explore the potential of weak supervision in a deep learning-based label prediction model. The goal was to use this model to extract labels from German free-text thoracic radiology reports on chest X-ray images and for training chest X-ray classification models.The proposed label extraction model for German thoracic radiology reports uses a German BERT encoder as a backbone and classifies a report based on the CheXpert labels. For investigating the efficient use of manually annotated data, the model was trained using manual annotations, weak rule-based labels, and both. Rule-based labels were extracted from 66071 retrospectively collected radiology reports from 2017-2021 (DS 0), and 1091 reports from 2020-2021 (DS 1) were manually labeled according to the CheXpert classes. Label extraction performance was evaluated with respect to mention extraction, negation detection, and uncertainty detection by measuring F1 scores. The influence of the label extraction method on chest X-ray classification was evaluated on a pneumothorax data set (DS 2) containing 6434 chest radiographs with associated reports and expert diagnoses of pneumothorax. For this, DenseNet-121 models trained on manual annotations, rule-based and deep learning-based label predictions, and publicly available data were compared.The proposed deep learning-based labeler (DL) performed on average considerably stronger than the rule-based labeler (RB) for all three tasks on DS 1 with F1 scores of 0.938 vs. 0.844 for mention extraction, 0.891 vs. 0.821 for negation detection, and 0.624 vs. 0.518 for uncertainty detection. Pre-training on DS 0 and fine-tuning on DS 1 performed better than only training on either DS 0 or DS 1. Chest X-ray pneumothorax classification results (DS 2) were highest when trained with DL labels with an area under the receiver operating curve (AUC) of 0.939 compared to RB labels with an AUC of 0.858. Training with manual labels performed slightly worse than training with DL labels with an AUC of 0.934. In contrast, training with a public data set resulted in an AUC of 0.720.Our results show that leveraging a rule-based report labeler for weak supervision leads to improved labeling performance. The pneumothorax classification results demonstrate that our proposed deep learning-based labeler can serve as a substitute for manual labeling requiring only 1000 manually annotated reports for training. · The proposed deep learning-based label extraction model for German thoracic radiology reports performs better than the rule-based model.. · Training with limited supervision outperformed training with a small manually labeled data set.. · Using predicted labels for pneumothorax classification from chest radiographs performed equally to using manual annotations.. Wollek A, Haitzer P, Sedlmeyr T et al. Language modelbased labeling of German thoracic radiology reports. Fortschr Röntgenstr 2024; DOI 10.1055/a-2287-5054.

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions.

PURPOSE: To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning. MATERIALS AND METHODS: Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks. The CVC/TT information is estimated by introducing a new multitask neural network architecture for jointly performing type/existence classification, course segmentation, and tip detection. Validation data consisted of 589 SCXRs that have been radiologically annotated for inserted TTs/CVCs, including an experts' categorical positioning assessment (reading 1). In-image positions of algorithm-detected TT/CVC tips could be corrected using a validation software tool (reading 2) that finally allowed for localization accuracy quantification. Algorithmic detection of images with misplaced devices (reading 1 as reference standard) was quantified by receiver operating characteristics. RESULTS: Supine chest radiographs were correctly classified according to inserted TTs/CVCs in 100%/98% of the cases, thereby with high accuracy in also spatially localizing the medical device tips: corrections less than 3 mm in >86% (TTs) and 77% (CVCs) of the cases. Chest radiographs with malpositioned devices were detected with area under the curves of >0.98 (TTs), >0.96 (CVCs with accidental vessel turnover), and >0.93 (also suboptimal CVC insertion length considered). The receiver operating characteristics limitations regarding CVC assessment were mainly caused by limitations of the applied CXR position definitions (region of interest derived from anatomical landmarks), not by algorithmic spatial detection inaccuracies. CONCLUSIONS: The TT and CVC tips were accurately localized in SCXRs by the presented algorithms, but triaging applications for CVC positioning assessment still suffer from the vague definition of optimal CXR positioning. Our algorithm, however, allows for an adjustment of these criteria, theoretically enabling them to meet user-specific or patient subgroups requirements. Besides CVC tip analysis, future work should also include specific course analysis for accidental vessel turnover detection.