Detection, Classification, and Segmentation of Rib Fractures From CT Data Using Deep Learning Models:A Review of Literature and Pooled Analysis

Purpose: Trauma-induced rib fractures are common injuries. The gold standard for diagnosing rib fractures is computed tomography (CT), but the sensitivity in the acute setting is low, and interpreting CT slices is labor-intensive. This has led to the development of new diagnostic approaches leveraging deep learning (DL) models. This systematic review and pooled analysis aimed to compare the performance of DL models in the detection, segmentation, and classification of rib fractures based on CT scans. Materials and Methods: A literature search was performed using various databases for studies describing DL models detecting, segmenting, or classifying rib fractures from CT data. Reported performance metrics included sensitivity, false-positive rate, F1-score, precision, accuracy, and mean average precision. A meta-analysis was performed on the sensitivity scores to compare the DL models with clinicians. Results: Of the 323 identified records, 25 were included. Twenty-one studies reported on detection, four on segmentation, and 10 on classification. Twenty studies had adequate data for meta-analysis. The gold standard labels were provided by clinicians who were radiologists and orthopedic surgeons. For detecting rib fractures, DL models had a higher sensitivity (86.7%; 95% CI: 82.6%-90.2%) than clinicians (75.4%; 95% CI: 68.1%-82.1%). In classification, the sensitivity of DL models for displaced rib fractures (97.3%; 95% CI: 95.6%-98.5%) was significantly better than that of clinicians (88.2%; 95% CI: 84.8%-91.3%). Conclusions: DL models for rib fracture detection and classification achieved promising results. With better sensitivities than clinicians for detecting and classifying displaced rib fractures, the future should focus on implementing DL models in daily clinics.</p

What Surgical Technique to Perform for Isolated Ulnar Shortening Osteotomy After Distal Radius Malunion:A Systematic Review

Background: Unstable fractures of the distal radius fractures (DRFs) may result in malunion, usually consisting of subsequent shortening and angular deviations. Ulnar shortening osteotomy (USO) is assumed to be a simpler procedure than radial correction osteotomy, resulting in fewer complications and comparable outcomes. The aim of this study was to identify the best surgical technique to perform USO to restore distal radioulnar joint congruency after DRF malunion. Methods: A systematic review of the literature is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines in February 2022 to identify studies reporting outcomes and surgical technique for isolated USO. The primary outcome was complication rates. Secondary outcomes included functional, radiologic, and patient-rated outcomes. The methodological index for nonrandomized studies criteria were used to assess the quality of evidence. Results: Included were 12 cohorts (185 participants). Due to substantial heterogeneity, a meta-analysis could not be performed. The overall complication rate was 33% (95% confidence interval, 16% to 51%). The most reported complication was implant irritation (22%), often requiring removal of the implant (13%). Only 3% nonunions were mentioned. Functional and patient-rated outcomes improved in most patients after USO. Quality of evidence of the papers was low to very low. Common methodological flaws were related to retrospective research. Conclusion: No evident differences in complication rates and functional outcomes between the surgical techniques were observed. Based on this literature, most complications are related to implant irritation. Nonunion and infection rates were rare. Therefore, a surgical technique with a buried implant might be preferred. This hypothesis requires further investigation.</p

An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs

PURPOSE: Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures. This study aims to internally and externally validate an open source algorithm for the detection and localization of DRFs. METHODS: Patients from a level 1 trauma center from Adelaide, Australia that presented between 2016 and 2020 with wrist trauma were retrospectively included. Radiographs were reviewed confirming the presence or absence of a fracture, as well as annotating radius, ulna, and fracture location. An internal validation dataset from the same hospital was created. An external validation set was created with two other level 1 trauma centers, from Groningen and Rotterdam, the Netherlands. Three surgeons reviewed both sets for DRFs. RESULTS: The algorithm was trained on 659 radiographs. The internal validation set included 190 patients, showing an accuracy of 87% and an AUC of 0.93 for DRF detection. The external validation set consisted of 188 patients, with an accuracy and AUC were 82% and 0.88 respectively. Radial and ulnar bone segmentation on the internal validation was excellent with an AP50 of 99 and 98, but moderate for fracture segmentation with an AP50 of 29. For external validation the AP50 was 92, 89 and 25 for radius, ulna, and fracture respectively. CONCLUSION: This open-source algorithm effectively detects DRFs with high accuracy and localizes them with moderate accuracy. It can assist clinicians in diagnosing suspected DRFs and is the first radiograph-based CNN externally validated on patients from multiple hospitals.</p

AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review

Purpose: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools’ accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs. Methods: A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS). Results: Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73–100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs. Conclusion: AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms.</p

Can CT-based gap and step-off displacement predict outcome after nonoperative treatment of acetabular fractures?

Aims: The aim of this study was to investigate the association between fracture displacement and survivorship of the native hip joint without conversion to a total hip arthroplasty (THA), and to determine predictors for conversion to THA in patients treated nonoperatively for acetabular fractures. Methods:A multicentre cross-sectional study was performed in 170 patients who were treated nonoperatively for an acetabular fracture in three level 1 trauma centres. Using the post-injury diagnostic CT scan, the maximum gap and step-off values in the weightbearing dome were digitally measured by two trauma surgeons. Native hip survival was reported using Kaplan-Meier curves. Predictors for conversion to THA were determined using Cox regression analysis. Results: Of 170 patients, 22 (13%) subsequently received a THA. Native hip survival in patients with a step-off ≤ 2 mm, &gt; 2 to 4 mm, or &gt; 4 mm differed at five-year follow-up (respectively: 94% vs 70% vs 74%). Native hip survival in patients with a gap ≤ 2 mm, &gt; 2 to 4 mm, or &gt; 4 mm differed at five-year follow-up (respectively: 100% vs 84% vs 78%). Step-off displacement &gt; 2 mm (&gt; 2 to 4 mm hazard ratio (HR) 4.9, &gt; 4 mm HR 5.6) and age &gt; 60 years (HR 2.9) were independent predictors for conversion to THA at follow-up. Conclusion: Patients with minimally displaced acetabular fractures who opt for nonoperative fracture treatment may be informed that fracture displacement (e.g. gap and step-off) up to 2 mm, as measured on CT images, results in limited risk on conversion to THA. Step-off ≥ 2 mm and age &gt; 60 years are predictors for conversion to THA and can be helpful in the shared decision-making process.</p

AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review

Purpose: Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools’ accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs. Methods: A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS). Results: Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73–100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs. Conclusion: AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms

An AI supported case study applying in vitro studies using the ONTOX toolbox: Protocol for a probabilistic risk assessment of perfluoroctanoic acid (PFOA)

The project ‘Ontology-driven and artificial intelligence-based repeated dose toxicity testing of chemicals for next generation risk assessment’ (ONTOX) under the EU programme Horizon 2020 is running from 01.05.21 to 30.04.26 and is coordinated by Vrije Universiteit, Brussel, Belgium (project website, URL: ONTOX project). The vision of ONTOX is to provide a functional and sustainable solution for advancing human risk assessment of chemicals without the use of animals in line with the principles of 21st century toxicity testing and next generation risk assessment. ONTOX will perform a case study on probabilistic risk assessment (PRA) on the selected chemical perfluoroctanoic acid (PFOA). The exposure assessment will use already established methods from the newly published scoping review, “Accessible methods and tools to estimate chemical exposure in humans to support risk assessment: a systematic scoping review”, or custom-made methods using R. The hazard characterisation will use some published methods as a starting point which will be adjusted and combined to fit this case study. The hazard identification/characterisation will use data from published literature and on in vitro data produced in ONTOX. The whole ONTOX toolbox will be used in this risk assessment, such as physiological based kinetic (PBK) models, quantitative in vivo in vitro extrapolation (QIVIVE), physiological maps (PMs) and boolean models , and a large transformer-based AI model. This is a protocol for the case study on PFOA, which will provide a proof-of-principle of PRA using in vitro studies.publishedVersio

Spacers and Valved Holding Chambers—The Risk of Switching to Different Chambers

© 2020 Spacers are pressurized metered-dose inhaler (pMDI) accessory devices developed to reduce problems of poor inhaler technique with pMDIs. Spacers that feature a 1-way inspiratory valve are termed valved holding chambers (VHCs); they act as aerosol reservoirs, allowing the user to actuate the pMDI device and then inhale the medication in a 2-step process that helps users overcome challenges in coordinating pMDI actuation with inhalation. Both spacers and VHCs have been shown to increase fine particle delivery to the lungs, decrease oropharyngeal deposition, and reduce corticosteroid-related side effects such as throat irritation, dysphonia, and oral candidiasis commonly seen with the use of pMDIs alone. Spacers and VHCs are not all the same, and also are not interchangeable: the performance may vary according to their size, shape, material of manufacture and propensity to become electrostatically charged, their mode of interface with the patient, and the presence or otherwise of valves and feedback devices. Thus, pairing of a pMDI plus a spacer or a VHC should be considered as a unique delivery system. In this Rostrum we discuss the risk potential for a patient getting switched to a spacer or VHC that delivers a reduced dose medication