Warfarin Toxicity and Individual Variability—Clinical Case

Warfarin is a widely used anticoagulant in the treatment and prevention of thrombosis, in the treatment for chronic atrial fibrillation, mechanical valves, pulmonary embolism, and dilated cardiomyopathy. It is tasteless and colorless, was used as a poison, and is still marketed as a pesticide against rats and mice. Several long-acting warfarin derivatives—superwarfarin anticoagulants—such as brodifacoum, diphenadione, chlorophacinone, bromadiolone, are used as pesticides and can produce profound and prolonged anticoagulation. Several factors increase the risk of warfarin toxicity. However, polymorphisms in cytochrome P450 genes and drug interactions account for most of the risk for toxicity complications. Each person is unique in their degree of susceptibility to toxic agents. The toxicity interpretation and the health risk of most toxic substances are a subject of uncertainty. Genetically determined low metabolic capacity in an individual can dramatically alter the toxin and metabolite levels from those normally expected, which is crucial for drugs with a narrow therapeutic index, like warfarin. Personalized approaches in interpretation have the potential to remove some of the scientific uncertainties in toxicity cases

Bone suppression increases the visibility of invasive pulmonary aspergillosis in chest radiographs

Objective: Chest radiographs (CXR) are an important diagnostic tool for the detection of invasive pulmonary aspergillosis (IPA) in critically ill patients, but their diagnostic value is limited by a poor sensitivity. By using advanced image processing, the aim of this study was to increase the value of chest radiographs in the diagnostic work up of neutropenic patients who are suspected of IPA. Methods: The frontal CXRs of 105 suspected cases of IPA were collected from four institutions. Radiographs could contain single or multiple sites of infection. CT was used as reference standard. Five radiologists and two residents participated in an observer study for the detection of IPA on CXRs with and without bone suppressed images (ClearRead BSI 3.2; Riverain Technologies). The evaluation was performed separately for the right and left lung, resulting in 78 diseased cases (or lungs) and 132 normal cases (or lungs). For each image, observers scored the likelihood of focal infectious lesions being present on a continuous scale (0-100). The area under the receiver operating characteristics curve (AUC) served as the performance measure. Sensitivity and specificity were calculated by considering only the lungs with a suspiciousness score of greater than 50 to be positive. Results: The average AUC for only CXRs was 0.815. Performance significantly increased, to 0.853, when evaluation was aided with BSI (p = 0.01). Sensitivity increased from 49% to 66% with BSI, while specificity decreased from 95% to 90%. Conclusion: The detection of IPA in CXRs can be improved when their evaluation is aided by bone suppressed images. BSI improved the sensitivity of the CXR examination, outweighing a small loss in specificity

How AI should be used in radiology: assessing ambiguity and completeness of intended use statements of commercial AI products

Abstract Background Intended use statements (IUSs) are mandatory to obtain regulatory clearance for artificial intelligence (AI)-based medical devices in the European Union. In order to guide the safe use of AI-based medical devices, IUSs need to contain comprehensive and understandable information. This study analyzes the IUSs of CE-marked AI products listed on AIforRadiology.com for ambiguity and completeness. Methods We retrieved 157 IUSs of CE-marked AI products listed on AIforRadiology.com in September 2022. Duplicate products (n = 1), discontinued products (n = 3), and duplicate statements (n = 14) were excluded. The resulting IUSs were assessed for the presence of 6 items: medical indication, part of the body, patient population, user profile, use environment, and operating principle. Disclaimers, defined as contra-indications or warnings in the IUS, were identified and compared with claims. Results Of 139 AI products, the majority (n = 78) of IUSs mentioned 3 or less items. IUSs of only 7 products mentioned all 6 items. The intended body part (n = 115) and the operating principle (n = 116) were the most frequently mentioned components, while the intended use environment (n = 24) and intended patient population (n = 29) were mentioned less frequently. Fifty-six statements contained disclaimers that conflicted with the claims in 13 cases. Conclusion The majority of IUSs of CE-marked AI-based medical devices lack substantial information and, in few cases, contradict the claims of the product. Critical relevance statement To ensure correct usage and to avoid off-label use or foreseeable misuse of AI-based medical devices in radiology, manufacturers are encouraged to provide more comprehensive and less ambiguous intended use statements. Key points • Radiologists must know AI products’ intended use to avoid off-label use or misuse. • Ninety-five percent (n = 132/139) of the intended use statements analyzed were incomplete. • Nine percent (n = 13) of the intended use statements held disclaimers contradicting the claim of the AI product. • Manufacturers and regulatory bodies must ensure that intended use statements are comprehensive. Graphical Abstrac

Bone suppression increases the visibility of invasive pulmonary aspergillosis in chest radiographs

textabstractObjective: Chest radiographs (CXR) are an important diagnostic tool for the detection of invasive pulmonary aspergillosis (IPA) in critically ill patients, but their diagnostic value is limited by a poor sensitivity. By using advanced image processing, the aim of this study was to increase the value of chest radiographs in the diagnostic work up of neutropenic patients who are suspected of IPA. Methods: The frontal CXRs of 105 suspected cases of IPA were collected from four institutions. Radiographs could contain single or multiple sites of infection. CT was used as reference standard. Five radiologists and two residents participated in an observer study for the detection of IPA on CXRs with and without bone suppressed images (ClearRead BSI 3.2; Riverain Technologies). The evaluation was performed separately for the right and left lung, resulting in 78 diseased cases (or lungs) and 132 normal cases (or lungs). For each image, observers scored the likelihood of focal infectious lesions being present on a continuous scale (0-100). The area under the receiver operating characteristics curve (AUC) served as the performance measure. Sensitivity and specificity were calculated by considering only the lungs with a suspiciousness score of greater than 50 to be positive. Results: The average AUC for only CXRs was 0.815. Performance significantly increased, to 0.853, when evaluation was aided with BSI (p = 0.01). Sensitivity increased from 49% to 66% with BSI, while specificity decreased from 95% to 90%. Conclusion: The detection of IPA in CXRs can be improved when their evaluation is aided by bone suppressed images. BSI improved the sensitivity of the CXR examination, outweighing a small loss in specificity

Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs

Purpose: To compare the performance of a convolutional neural network (CNN) to that of 11 radiologists in detecting scaphoid bone fractures on conventional radiographs of the hand, wrist, and scaphoid. Materials and Methods: At two hospitals (hospitals A and B), three datasets consisting of conventional hand, wrist, and scaphoid radiographs were retrospectively retrieved: a dataset of 1039 radiographs (775 patients [mean age, 48 years ± 23 {standard deviation}; 505 female patients], period: 2017–2019, hospitals A and B) for developing a scaphoid segmentation CNN, a dataset of 3000 radiographs (1846 patients [mean age, 42 years ± 22; 937 female patients], period: 2003–2019, hospital B) for developing a scaphoid fracture detection CNN, and a dataset of 190 radiographs (190 patients [mean age, 43 years ± 20; 77 female patients], period: 2011–2020, hospital A) for testing the complete fracture detection system. Both CNNs were applied consecutively: The segmentation CNN localized the scaphoid and then passed the relevant region to the detection CNN for fracture detection. In an observer study, the performance of the system was compared with that of 11 radiologists. Evaluation metrics included the Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC). Results: The segmentation CNN achieved a DSC of 97.4% ± 1.4 with an HD of 1.31 mm ± 1.03. The detection CNN had sensitivity of 78% (95% CI: 70, 86), specificity of 84% (95% CI: 77, 92), PPV of 83% (95% CI: 77, 90), and AUC of 0.87 (95% CI: 0.81, 0.91). There was no difference between the AUC of the CNN and that of the radiologists (0.87 [95% CI: 0.81, 0.91] vs 0.83 [radiologist range: 0.79–0.85]; P = .09). Conclusion: The developed CNN achieved radiologist-level performance in detecting scaphoid bone fractures on conventional radiographs of the hand, wrist, and scaphoid

Development and Validation of a Convolutional Neural Network for Automated Detection of Scaphoid Fractures on Conventional Radiographs

Purpose: To compare the performance of a convolutional neural network (CNN) to that of 11 radiologists in detecting scaphoid bone fractures on conventional radiographs of the hand, wrist, and scaphoid. Materials and Methods: At two hospitals (hospitals A and B), three datasets consisting of conventional hand, wrist, and scaphoid radiographs were retrospectively retrieved: a dataset of 1039 radiographs (775 patients [mean age, 48 years ± 23 {standard deviation}; 505 female patients], period: 2017–2019, hospitals A and B) for developing a scaphoid segmentation CNN, a dataset of 3000 radiographs (1846 patients [mean age, 42 years ± 22; 937 female patients], period: 2003–2019, hospital B) for developing a scaphoid fracture detection CNN, and a dataset of 190 radiographs (190 patients [mean age, 43 years ± 20; 77 female patients], period: 2011–2020, hospital A) for testing the complete fracture detection system. Both CNNs were applied consecutively: The segmentation CNN localized the scaphoid and then passed the relevant region to the detection CNN for fracture detection. In an observer study, the performance of the system was compared with that of 11 radiologists. Evaluation metrics included the Dice similarity coefficient (DSC), Hausdorff distance (HD), sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC). Results: The segmentation CNN achieved a DSC of 97.4% ± 1.4 with an HD of 1.31 mm ± 1.03. The detection CNN had sensitivity of 78% (95% CI: 70, 86), specificity of 84% (95% CI: 77, 92), PPV of 83% (95% CI: 77, 90), and AUC of 0.87 (95% CI: 0.81, 0.91). There was no difference between the AUC of the CNN and that of the radiologists (0.87 [95% CI: 0.81, 0.91] vs 0.83 [radiologist range: 0.79–0.85]; P = .09). Conclusion: The developed CNN achieved radiologist-level performance in detecting scaphoid bone fractures on conventional radiographs of the hand, wrist, and scaphoid

Subtlety categories.

<p>Number of cases classified into the different subtlety categories.</p><p>Subtlety categories.</p

Patient demographics.

<p>Age, gender, average time between the chest radiograph and the CT in days, and projection type are displayed for the diseased and normal cases. n  =  number.</p><p>Patient demographics.</p

Number of, on average, detected cases per subtlety category.

<p>Number of, on average, detected cases per subtlety category.</p