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
The use of robots in orthopaedic surgery is an
emerging field that is gaining momentum. It has the potential for significant
improvements in surgical planning, accuracy of component implantation
and patient safety. Advocates of robot-assisted systems describe
better patient outcomes through improved pre-operative planning
and enhanced execution of surgery. However, costs, limited availability,
a lack of evidence regarding the efficiency and safety of such systems
and an absence of long-term high-impact studies have restricted
the widespread implementation of these systems. We have reviewed
the literature on the efficacy, safety and current understanding of
the use of robotics in orthopaedics. Cite this article:
Surgeons need to be able to measure angles and distances in three dimensions in the planning and assessment of knee replacement. Computed tomography (CT) offers the accuracy needed but involves greater radiation exposure to patients than traditional long-leg standing radiographs, which give very little information outside the plane of the image. There is considerable variation in CT radiation doses between research centres, scanning protocols and individual scanners, and ethics committees are rightly demanding more consistency in this area. By refining the CT scanning protocol we have reduced the effective radiation dose received by the patient down to the equivalent of one long-leg standing radiograph. Because of this, it will be more acceptable to obtain the three-dimensional data set produced by CT scanning. Surgeons will be able to document the impact of implant position on outcome with greater precision.
We present a retrospective review of a single-surgeon series of 30 consecutive lengthenings in 27 patients with congenital short femur using the Ilizarov technique performed between 1994 and 2005. The mean increase in length was 5.8 cm/18.65% (3.3 to 10.4, 9.7% to 48.8%), with a mean time in the frame of 223 days (75 to 363). By changing from a distal to a proximal osteotomy for lengthening, the mean range of knee movement was significantly increased from 98.1° to 124.2° (p = 0.041) and there was a trend towards a reduced requirement for quadricepsplasty, although this was not statistically significant (p = 0.07). The overall incidence of regenerate deformation or fracture requiring open reduction and internal fixation was similar in the distal and proximal osteotomy groups (56.7% and 53.8%, respectively). However, in the proximal osteotomy group, pre-placement of a Rush nail reduced this rate from 100% without a nail to 0% with a nail (p <
0.001). When comparing a distal osteotomy with a proximal one over a Rush nail for lengthening, there was a significant decrease in fracture rate from 58.8% to 0% (p = 0.043). We recommend that in this group of patients lengthening of the femur with an Ilizarov construct be carried out through a proximal osteotomy over a Rush nail. Lengthening should also be limited to a maximum of 6 cm during one treatment, or 20% of the original length of the femur, in order to reduce the risk of complications.