The worldwide COVID-19 pandemic is directly impacting the field of orthopaedic surgery and traumatology with postponed operations, changed status of planned elective surgeries and acute emergencies in patients with unknown infection status. To this point, Germany's COVID-19 infection numbers and death rate have been lower than those of many other nations. This article summarizes the current regimen used in the field of orthopaedics in Germany during the COVID-19 pandemic. Internal university clinic guidelines, latest research results, expert consensus, and clinical experiences were combined in this article guideline.Aims
Methods
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
Methods
