Aims. The aticularis genu (AG) is the least substantial and deepest muscle of the anterior compartment of the thigh and of uncertain significance. The aim of the study was to describe the anatomy of AG in cadaveric specimens, to characterize the relevance of AG in pathological distal femur specimens, and to correlate the anatomy and pathology with preoperative magnetic resonance imaging (MRI) of AG. Methods. In 24 cadaveric specimens, AG was identified, photographed, measured, and dissected including neurovascular supply. In all, 35 resected distal femur specimens were examined. AG was photographed and measured and its utility as a surgical margin examined. Preoperative MRIs of these cases were retrospectively analyzed and assessed and its utility assessed as an anterior soft tissue margin in surgery. In all cadaveric specimens, AG was identified as a substantial structure, deep and separate to vastus itermedius (VI) and separated by a clear fascial plane with a discrete neurovascular supply. Mean length of AG was 16.1 cm ( ± 1.6 cm) origin anterior aspect distal third femur and insertion into suprapatellar bursa. In 32 of 35 pathological specimens, AG was identified (mean length 12.8 cm ( ± 0.6 cm)). Where AG was used as anterior cover in pathological specimens all surgical margins were clear of disease. Of these cases, preoperative MRI identified AG in 34 of 35 cases (mean length 8.8 cm ( ± 0.4 cm)). Results. AG was best visualized with T1-weighted axial images providing sufficient cover in 25 cases confirmed by pathological findings.These results demonstrate AG as a discrete and substantial muscle of the anterior compartment of the thigh, deep to VI and useful in providing anterior soft tissue margin in distal femoral resection in bone tumours. Conclusion. Preoperative assessment of cover by AG may be useful in predicting cases where AG can be dissected, sparing the remaining quadriceps muscle, and therefore function. Cite this article: Bone Joint Open 2020;1-9:585–593

Aims

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.