Patella resurfacing in total knee arthroplasty is a contentious issue. The literature suggests that resurfacing of the patella is based on surgeon preference, and little is known about the role and timing of resurfacing and how this affects outcomes. We analyzed 134,799 total knee arthroplasties using data from the Australian Orthopaedic Association National Joint Replacement Registry. Hazards ratios (HRs) were used to compare rates of early revision between patella resurfacing at the primary procedure (the resurfacing group, R) and primary arthroplasty without resurfacing (no-resurfacing group, NR). We also analyzed the outcomes of NR that were revised for isolated patella addition. At 5 years, the R group showed a lower revision rate than the NR group: cumulative per cent revision (CPR) 3.1% and 4.0%, respectively (HR = 0.75, p < 0.001). Revisions for patellofemoral pain were more common in the NR group (17%) than in the R group (1%), and "patella only" revisions were more common in the NR group (29%) than in the R group (6%). Non-resurfaced knees revised for isolated patella addition had a higher revision rate than patella resurfacing at the primary procedure, with a 4-year CPR of 15% and 2.8%, respectively (HR = 4.1, p < 0.001). Rates of early revision of primary total knees were higher when the patella was not resurfaced, and suggest that surgeons may be inclined to resurface later if there is patellofemoral pain. However, 15% of non-resurfaced knees revised for patella addition are re-revised by 4 years. Our results suggest an early beneficial outcome for patella resurfacing at primary arthroplasty based on revision rates up to 5 years.

Background: As an adjunct to non-operative management, splenic artery embolization (SAE) has been increasingly utilized throughout the world and is now the standard of care for hemodynamically stable patients. This study aimed to retrospectively assess the rate of splenic salvage and complications after SAE for blunt trauma at a level 1 trauma center using the 2018 update to the AAST criteria, and further sub-stratify the role of angiography in AAST grade III injuries with significant hemoperitoneum. All patients between 1 January 2009 and 1 January 2019 who underwent blunt trauma and proceeded to embolization were included. Data was collected concerning initial injury grade, location of embolization, type of embolic material used, complications, and need for subsequent splenectomy. Technical success was defined as successful angiographic occlusion of the target artery at the conclusion of embolization. Clinical success was defined as splenic salvage at discharge. Vascular lesions were characterized including those with active bleeding, pseudoaneurysm, and arterio-venous fistula. Results: Two hundred thirty-two patients were included in the study. Treatments were performed at a median of 0 days (range 0–28 days) and the median AAST grade was IV (range III-V). Technical success was achieved in all patients. There were 13 complications (5.6%) consisting of re-bleed (9, 3.9%), infarction (3, 1.3%), and access site haematoma (1, 0.43%). Clinical success was achieved in 97% of patients with 7 patients requiring splenectomy after SAE (3.0%) at a median time of 4 days (range 0–17 days). Angiography in patients with grade III injuries identified 18 occult vascular injuries not identified at initial CT (p < 0.0001). Conclusions: The SPLEEN-IN study shows that treatment of intermediate-high grade blunt force traumatic splenic injuries using SAE resulted in a low rate of complication and splenic salvage in 97% of patients, providing a safe and effective treatment in stable patients. In addition, angiography of grade III injuries identified occult vascular lesions and may warrant treatment of select patients in this cohort. Level of evidence: Level 3.

Purpose: To assess the efficacy of conservative management and embolisation in patients with spontaneous retroperitoneal haemorrhage. Methods: Single-centre retrospective case–control study of patients with spontaneous retroperitoneal haemorrhage treated conservatively or with embolisation. Patients aged ≥ 18 years were identified from CT imaging reports stating a diagnosis of retroperitoneal haemorrhage or similar and images reviewed for confirmation. Exclusion criteria included recent trauma, surgery, retroperitoneal vascular line insertion, or other non-spontaneous aetiology. Datapoints analysed included treatment approach (conservative or embolisation), technical success, clinical success, and mortality outcome. Results: A total of 54 patients met inclusion criteria, who were predominantly anticoagulated (74%), male (72%), older adults (mean age 69 years), with active haemorrhage on CT (52%). Overall mortality was 15%. Clinical success was more likely with conservative management (36/38) than embolisation (9/16; p < 0.01), and all-cause (1/38 vs 7/16; p < 0.01) and uncontrolled primary bleeding (1/38 vs 5/16; p < 0.01) mortality were higher with embolisation. However, embolised patients more commonly had active bleeding on CT (15/38 vs 13/16; p < 0.01), shock (5/38 vs 6/16; p < 0.04), and higher blood transfusion volumes (mean 2.2 vs 5.9 units; p < 0.01). After one-to-one propensity score matching, differences in clinical success (p = 0.04) and all-cause mortality (p = 0.01) remained; however, difference in uncontrolled primary bleeding mortality did not (p = 0.07). Conclusion: Conservative management of SRH is likely to be effective in most patients, even in those who are anticoagulated and haemodynamically unstable, with variable success seen after embolisation in a more unstable patient group, supporting the notion that resuscitation and optimisation of coagulation are the most vital components of treatment.

Introduction: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perception of radiologists, radiographers, medical students and patients on AI use in image reporting has been explored, there is limited literature on non‐radiologist clinicians' opinion on this topic.Method: Single‐centre online survey targeting non‐radiologist medical staff conducted from May to August 2021 at a tertiary referral hospital in Melbourne, Australia. Survey questions revolved around clinicians' level of comfort acting on AI‐generated reports with varying levels of radiologist involvement and scan complexity, opinion on medicolegal responsibility for erroneous AI‐issued reports and perception of data privacy and security.Results: Eighty‐eight responses were collected, including 47.9% of consultants. Non‐radiologist clinicians across all seniorities and specialties felt significantly less comfortable acting on AI‐issued reports compared with radiologist‐issued reports (mean comfort radiologist 6.44/7, mean comfort AI 3.35/7, P < 0.001) but felt equally comfortable with an AI‐hybrid model of care (mean comfort hybrid 6.38/7, P = 0.676). Non‐radiologist clinicians believed that medicolegal responsibility with errors in AI‐issued reports mostly lay with hospitals or health service providers (65.9%) and radiologists (54.5%). Regarding data privacy and security, non‐radiologist clinicians felt significantly less comfortable with AI issuing image reports instead of radiologists (P < 0.001).Conclusion: A hybrid AI‐generated radiologist‐confirmed method of image reporting may be the ideal way of integrating AI into clinical practice based on the perception of our referring non‐radiologist medical colleagues. Formal guidelines on medicolegal responsibility and data privacy should be established prior to utilising AI in the clinical setting.

Objectives: Trauma chest radiographs may contain subtle and time-critical pathology. Artificial intelligence (AI) may aid in accurate reporting, timely identification and worklist prioritisation. However, few AI programs have been externally validated. This study aimed to evaluate the performance of a commercially available deep convolutional neural network – Annalise CXR V1.2 (Annalise.ai) – for detection of traumatic injuries on supine chest radiographs. Methods: Chest radiographs with a CT performed within 24 h in the setting of trauma were retrospectively identified at a level one adult trauma centre between January 2009 and June 2019. Annalise.ai assessment of the chest radiograph was compared to the radiologist report of the chest radiograph. Contemporaneous CT report was taken as the ground truth. Agreement with CT was measured using Cohen’s κ and sensitivity/specificity for both AI and radiologists were calculated. Results: There were 1404 cases identified with a median age of 52 (IQR 33–69) years, 949 males. AI demonstrated superior performance compared to radiologists in identifying pneumothorax (p = 0.007) and segmental collapse (p = 0.012) on chest radiograph. Radiologists performed better than AI for clavicle fracture (p = 0.002), humerus fracture (p < 0.0015) and scapula fracture (p = 0.014). No statistical difference was found for identification of rib fractures and pneumomediastinum. Conclusion: The evaluated AI performed comparably to radiologists in interpreting chest radiographs. Further evaluation of this AI program has the potential to enable it to be safely incorporated in clinical processes. Advances in knowledge: Clinically useful AI programs represent promising decision support tools.