Non-surgical correction of knee flexion deformity in persons with haemophilia: A staged multidisciplinary approach.

INTRODUCTION: Flexion deformity of the knee is a common complication following recurrent haemarthrosis in persons with haemophilia (PWH) on episodic factor replacement therapy, restricting independent mobility. There is limited literature on the comprehensive management of this condition. This report provides the outcome of a staged multidisciplinary approach for the correction of knee flexion deformity (KFD) even in limited resource settings. PATIENTS AND METHODS: The data of 49 consecutive PWH who were treated for KFD were analysed. The approach included graded physical therapy (PT), followed by serial casting and/or mobilisation under anaesthesia (MUA). MUA was done in carefully selected knees. Surgical correction was opted when non-surgical methods failed. RESULTS: Of the 49 patients (55 knees), with a median KFD of 40 degrees (range: 10-90), 26/55 (47%) were corrected by graded PT. With serial casting, 9/19 (47%) knees had their KFD corrected. MUA was done for 11 knees of which five achieved correction (45%). Surgical correction was required for only seven knees (12.7%). Following this approach, KFD improved from 40 degrees (range: 10-90) to 15 degrees (range: 0-40), with only minor loss of flexion from 105 (range: 60-155) to 90 degrees (range: 30-150). Out of 55 KFD, 46 (83.6%) KFD were corrected; non-surgical, 39 (70.9%) and surgery, seven (12.7%). The remaining patients (nine KFD; 16.4%) were able to achieve their functional goal despite not meeting the correction criteria. CONCLUSION: This study shows that in PWH, functionally significant KFD correction can be achieved in about 71%, through non-surgical methods, even without prophylactic factor replacement.

Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study.

In medical practice, chest X-rays are the most ubiquitous diagnostic imaging tests. However, the current workload in extensive health care facilities and lack of well-trained radiologists is a significant challenge in the patient care pathway. Therefore, an accurate, reliable, and fast computer-aided diagnosis (CAD) system capable of detecting abnormalities in chest X-rays is crucial in improving the radiological workflow. In this prospective multicenter quality-improvement study, we have evaluated whether artificial intelligence (AI) can be used as a chest X-ray screening tool in real clinical settings. Methods: A team of radiologists used the AI-based chest X-ray screening tool (qXR) as a part of their daily reporting routine to report consecutive chest X-rays for this prospective multicentre study. This study took place in a large radiology network in India between June 2021 and March 2022. Results: A total of 65,604 chest X-rays were processed during the study period. The overall performance of AI achieved in detecting normal and abnormal chest X-rays was good. The high negatively predicted value (NPV) of 98.9% was achieved. The AI performance in terms of area under the curve (AUC), NPV for the corresponding subabnormalities obtained were blunted CP angle (0.97, 99.5%), hilar dysmorphism (0.86, 99.9%), cardiomegaly (0.96, 99.7%), reticulonodular pattern (0.91, 99.9%), rib fracture (0.98, 99.9%), scoliosis (0.98, 99.9%), atelectasis (0.96, 99.9%), calcification (0.96, 99.7%), consolidation (0.95, 99.6%), emphysema (0.96, 99.9%), fibrosis (0.95, 99.7%), nodule (0.91, 99.8%), opacity (0.92, 99.2%), pleural effusion (0.97, 99.7%), and pneumothorax (0.99, 99.9%). Additionally, the turnaround time (TAT) decreased by about 40.63% from pre-qXR period to post-qXR period. Conclusions: The AI-based chest X-ray solution (qXR) screened chest X-rays and assisted in ruling out normal patients with high confidence, thus allowing the radiologists to focus more on assessing pathology on abnormal chest X-rays and treatment pathways.