Assessment of Preoperative Planning and Intraoperative Accuracy of the AIKNEE System for Total Knee Arthroplasty

doi:10.21203/rs.3.rs-3229201/v1

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Research Article

Assessment of Preoperative Planning and Intraoperative Accuracy of the AIKNEE System for Total Knee Arthroplasty

https://doi.org/10.21203/rs.3.rs-3229201/v1

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Journal Publication

published 19 Jul, 2024

Read the published version in BMC Musculoskeletal Disorders →

You are reading this latest preprint version

Background

The aim of this retrospective study was to evaluate the effectiveness and accuracy of the AIKNEE system in preoperative planning and intraoperative alignment for total knee arthroplasty (TKA).

Methods

A total of 64 patients were planned preoperatively by the AIKNEE system, including the measurement of mechanical femorotibial angle (mFTA), lateral distal femoral angle (LDFA), and medial proximal tibial angle (MPTA) using three-dimensional reconstructed images. Intraoperatively, the actual prosthesis size and alignment were compared to the planned parameters. Postoperative outcomes, including pain levels, range of motion (ROM), and Knee Scoring System (KSS) scores, were assessed after surgery. Statistical analyses were performed to evaluate the correlation between alignment deviations and postoperative function.

Results

The AIKNEE system accurately predicted the prosthesis size in thirty-one of femoral cases (48%) and forty-seven of tibial cases (73%). Deviations of mFTA, LDFA, and MPTA from the target value were within 3° in 88%, 92%, and 95% of cases, respectively. A significant improvement was observed in postoperative pain, ROM, and KSS scores (p < 0.001). Correlation analysis revealed that greater deviations in mFTA and LDFA were associated with increased pain (p = 0.004, 0.047) and lower KSS scores (p = 0.027).

Conclusion

The AIKNEE system demonstrated promising results in predicting prosthesis size and achieved alignment within the desired range in a majority of cases. Postoperative outcomes, including pain levels and functional improvement, were favorable.

Total knee arthroplasty

AIKNEE system

Preoperative planning

Intraoperative accuracy

Postoperative outcomes

The preoperative planning of precise is advantageous for promoting knee prosthesis alignment, minimizing postoperative pain, and enhancing patient satisfaction ^[1–4]. X-ray and computed tomography (CT) imaging techniques offer crucial data for preoperative planning. While X-ray imaging is economical and convenient, it necessitates a high level of expertise from the radiologists. On the other hand, CT imaging is not constrained by the patient's position and imaging proficiency, and can be directly reconstructed using software to facilitate preoperative planning ^{[5, 6]}; Prediction of prosthesis sizes derived three-dimensional (3D) reconstructed knees are more sensitive and accurate than results from two-dimensional (2D) images ^[7].

In a clinical setting, 3D reconstruction of CT images helps formulate more reasonable surgical planning. Especially, it was combined with artificial intelligence, such as PSI devices and robotic-assisted, which are costly ^[7]. Intelligent autonomous planning is the further development of artificial intelligence technology, is the ultimate goal of mission planning, and also economical, convenient. Traditionally, Mimics software is commonly used for preoperative planning and require image segmentation, which is time-consuming and prone to human error ^[8]. CT Images are acquired and imported into the AIKNEE system, and a report is created. The report covers knee parameters, osteotomy thickness, and prosthesis sizes. At present, the clinical application of AIKNEE system is still rare, and its accuracy is still uncertain; Therefore, The study was to evaluate the effectiveness and accuracy of the AIKNEE system in preoperative planning and intraoperative alignment for total knee arthroplasty (TKA).

Patients

This was an institutional review board-approved retrospective study (The Second People's Hospital of Fujian Province, SPHFJP-K2022017-02). Patients who had perfected preoperative planning for AIKNEE and underwent total knee arthroplasty between January 2021 and October 2022 were included. All procedures were performed by a fellowship-trained orthopedic surgeon at an academic institution.

AIKNEE Workflow

Import DICOM images and reconstruct the knee joint in three-dimensions.

The AIKNEE system automatically identifies and labels the mechanical axis of the femur (line a), the anatomical axis of the femur (line b) and the mechanical axis of the tibia (line c) on the 3D reconstructed image (Fig. 1A). Measurement of angles such as mechanical femorotibial angle (mFTA), anatomical femorotibial angle (aFTA), anatomic mechanic axis deviation (AMA), posterior condylar axis (PCA), the lateral distal femoral angle (LDFA), and the medial proximal tibial angle (MPTA).

The AIKNEE system further analyzes the measured data to estimate the thickness of the femoral and tibial osteotomies, predict the size of the prosthesis, and simulate its placement (Fig. 1B). Review of mechanical axis alignment again (line a perpendicular to line d, line c perpendicular to line f and the angle between line a and line c is 180°), observe the fit of the prosthesis and generate a preoperative planning report. The surgeon refers to this report to draw up the surgical plan and for intraoperative reference.

Knee-related Parameters

mFTA: the angle between line a and line c, is a direct indicator of the observed alignment of the mechanical axis, which is maintained with a target of 180°; AMA: The angle between line a and line b (Fig. 1 ①) is an important indicator of the presence of extra-articular deformity, with a normal range of 5° to 7°. LDFA: the lateral angle between line b and line d (Fig. 1 ②); MPTA: the angle between line f and line c medially (Fig. 1 ③); LDFA and MPTA are aimed at 90° to achieve mechanical alignments. Deviation of each angle from the target value, such as mFTA, LDFA and MPTA, within 3° can effectively improve the survival rate of the prosthesis ^[9–12]. Deviations within 3° were designated as non-outliers in this study, and those beyond 3° as outliers.

Data Collection

Preoperative data: patient demographics age, sex, body mass index, Knee Scoring System (KSS) Score, Visual Analogue Score (VAS), range of motion (ROM)

KSS: an important index that responds to knee recovery after total knee arthroplasty ^{[13, 14]}. It was used to assess patients' VAS scores for walking on level ground, stairs or slopes, as well as knee stability, ROM and distance traveled. Knee function was assessed at preoperatively, postoperatively with the use of the KSS Score.

The difference between the postoperative mFTA, LDFA, MPTA and the surgical target value is marked as a D value. Analysis of the correlation between D values and postoperative ROM, VAS scores and KSS scores to observe the effect of D values on postoperative function.
Preoperative planning reports can be referenced before the preparation of prototype sizes. The AIKNEE predictive prosthesis sizes is prioritized. If the predicted size of the prosthesis is perfect match, only one insertions test is required; And more insertions for a deviation of more than 1 size, which is related to the experience of the surgeon. The enhancement of the accuracy rate of preoperative planning of prostheses sizes, decreases operation time and saves surgical resources, reduces the number of intraoperative prosthesis insertions, and promotes functional recovery after surgery ^{[9, 11]}.

Statistical Analysis

Statistical analyses were performed using the SPSS20.0 software. The measurement data were expressed as mean ± standard deviation (x ± s), and all were in line with approximate normal distribution or normal distribution. Comparisons of indexes before and after surgery within groups were performed using paired samples t-tests. The Chi-square test was used for qualitative values. Knee-related parameters were correlated with postoperative ROM, VAS score and KSS score using spearman correlation analysis. The statistical significance was set at p < 0.05 for all tests.

A total of 64 patients were included in the study, mean age 68.17±7.94 years, Body Mass Index (BMI) 26.52±4.22 kg/m². Postoperative mFTA was much closer to the target values of 180°; And all the KSS knee score, VAS score and ROM were significantly improved compared with preoperation (p < 0.001) (Table 1).

Table 1 Preoperative and postoperative patient conditions.

	mFTA	ROM	VAS	KSS
preoperative	189.31±5.08*	105.31±15.56	6.14±0.71	98.66±13.35
postoperative	181.21±2.40^#	116.56±6.42^#	0.27±0.48^#	148.67±8.20^#

VAS: visual analogue score;

KSS: knee society system score;

mFTA: mechanical femorotibial angle;

*: means that the preoperative mFTA is different from the target value by one-sample t-test (p < 0.05);

#: means that mFTA, ROM, VAS and KSS scores were statistically different (p < 0.05) in a paired samples t-test between Preoperative and postoperative.

88% of the mFTA, 92% of the LDFA and 95% of the MPTA angle were within the threshold of 3°. According to the results of correlation analysis, there was a positive correlation between the D value and the knee function (p < 0.05). The larger the mFTA deviation from target values, the lower the KSS scores, the poorer the knee function. (p = 0.027). The severity of pain intensity worse with increasing mFTA or LDFA divergence from target values (p = 0.004, 0.047) (Table 2).

Table 2 Correlation analysis of the postoperative outlier differences of each knee parameter with function.

		mFTA^a	LDFA^b	MPTA^c
Activity	r	-0.034	0.062	-0.001
	p	0.790	0.628	0.994
VAS scores	r	0.356	0.249	0.164
	p	0.004^*	0.047^*	0.196
KSS scores	r	-0.276	-0.062	-0.025
	p	0.027^*	0.628	0.844

VAS: visual analogue score;

KSS: knee society system score;

a: Difference between the postoperative mFTA and 180°;

b: postoperative difference between the postoperative LDFA and 90°;

c: the difference between the postoperative MPTA and 90°;

*:The data were statistically significant, p < 0.05.

The accuracy of femoral and tibial prosthesis were 48% and 73%, respectively; The mean intraoperative femoral side prosthetics were inserted 1.67±0.82 times, the tibial side were inserted 1.38±0.70 times; The number of tibial side prostheses was fewer (p = 0.009), and the tibial side prediction accuracy was greater (p = 0.004) (Table 3).

Table 3 Statistical analysis of the number of intraoperative prosthesis insertion trials.

insertion trials(time)	Femoral side (cases)	Tibial side (cases)	P
1	31（48%）	47（73%）	0.004
≤2	56（88%）	59（92%）	0.380
＞2	8（13%）	5（8%）	0.388

%: Percentage of cases.

In patients with more than 2 inserts of femoral and tibial prosthesis, 92% of patients had flexion contracture deformity; One of the patients had a flexion deformity of 15°, a lax medial collateral ligament, and the largest deviation in tibia and femur model. Consideration of flexion deformity and ligament relaxation may have an impact on its prediction.

Case report:

A 68-year-old woman admitted to our clinic with left knee pain and limitation in knee flexion. Admission check: The left knee had a flexion contracture of 15°, and the ROM of the left knee was 15° (extension) to 90° (flexion). The left knee had a mild valgus deformity, and instability was observed in the stress tests. The preoperative KSS score was 75. Preoperative planning predicted a prosthesis size of ATTUNE size-3 by the AIKNEE software (Figure 2). Intraoperatively, it was adjusted to an ATTUNE size-6 prosthesis and the medial collateral ligament was reconstructed. Postoperative review of the left knee X-ray (Figure 3). The patients started with active and passive ROM exercises from the first postoperative day. Follow-up of the patient was performed at 1-month, 3-months, and 6-months after surgery.

Mechanical alignment and prosthesis fit are key factors in postoperative pain and functional improvement. Michel P. Bonnin et al. found that CT was more accurate in parameters measurement such as mFTA and more optimal in predicting the size and position of the prosthesis ^[15–17]. The AIKNEE system measures the mFTA, LDFA, and MPTA by 3D CT reconstruction and predicts the prosthesis size, as an intraoperative reference; This study demonstrated that the mFTA are significantly related to postoperative pain and function, indicating that Mechanical alignment and prosthesis fit are closely related to postoperative pain.

Preoperative and postoperative functional changes

Understanding joint characteristics is crucial for improving surgical accuracy and prosthesis lifespan. It also serves as a crucial prerequisite for surgery. Because of this, preoperative parameter measurement accuracy is crucial. Compared to X-rays, CT imaging, the location of the patient, the projection angle and the experience of the photographer, are the key tools for osteopathic surgery ^{[18, 19]}. 3D CT reconstruction of the femur and tibia gives the surgeon more accurate preoperative data, prediction of osteotomy thickness and prosthesis size ^[7]; This information aids in the reconstruction of mechanical alignment and soft tissue balance during surgery, which helps the patient regain function. In this study the mFTA was 189.31 ± 5.08° preoperatively and 181.21 ± 2.40° after intraoperative correction of the valgus deformity (p < 0.001). The KSS score for knee function improved from 98.66 ± 13.35 to 148.67 ± 8.20, pain was reduced from 6.14 ± 0.71 to 0.27 ± 0.48, and ROM improved significantly. The quality of patient survival was improved (p < 0.001).

Correlation analysis of knee joint parameter outliers

Results of this study indicate that the mechanical axes of the lower limbs, represented by the D value of mFTA, correlated with pain and knee function. The D value of LDFA is associated with increased severity of pain. And the greater the D value, the greater deviation from target value, the worse pain. The alignment of the mechanical axis of the femur and tibia in relation to the osteotomy plane has a direct impact on the pressure distribution in the double compartment, which is closely associated with postoperative knee pain and functional activities ^{[20, 21]}. Furthermore, a significant proportion of the mFTA, LDFA, and MPTA measurements (88%, 92%, and 95% respectively) achieved the desired D value of less than 3°. Observation results of Michel P. Bonnin et al. showed that the Knee-Plan software (Symbios, Yverdon les bains, Switzerland) was utilized for preoperative planning, resulting in 84% of the D values for mFTA, LDFA, and MPTA being less than 3°, which is comparable to the findings of our study ^{[16, 22, 23]}.

Analysis of the fit of the knee sizes

Poor fit is often associated with complications such as loosening of the prosthesis and functional impairment ^{[4, 8, 24]}. Although using X-ray images to establish a two-dimensional model is quick and cheap, it cannot effectively forecast the trend of knee alignment in patients ^{[5, 25]}. The three-dimensional model demonstrates a high level of accuracy in predicting the actual implant size, with some studies reporting accuracy rates of up to 100%. In contrast, the two-dimensional digital model only predicts the actual implant size in approximately 50% of cases ^[7]. The AIKNEE system exhibits an accuracy rate of 48% for femoral prostheses and 73% for tibial prostheses. Notably, the accuracy on the tibial side is significantly higher than that on the femoral side (p = 0.004). Additionally, the insertion of the tibial side prosthesis is less frequent (p = 0.009), and the accuracy of the tibial side prediction surpasses that of the two-dimensional model. In comparison to Miura M.D. et al. the utilization of the MIMICS system for the prediction of femoral and tibial components prostheses yielded an accuracy of 44.3% and 57.00% respectively ^[8]. Conversely, the AIKNEE system demonstrated a significantly enhanced accuracy for tibial prostheses, while also offering the advantages of reduced time and simplified operation when compared to Mimics software, with an average time per case reduced from approximately 24 minutes to only about 5 minutes ^[26–28]. However, as indicated by Vicente J et al, the accuracy of tibial prostheses prediction reached 92%, while femoral prostheses prediction exceeded 95%, thus displaying a high level of consistency ^[29]. Kotela et al. discovered that MyKnee achieved an 89.8% accuracy rate in predicting femoral components and 75.8% accuracy rate in predicting tibial components ^[30]. In comparison to the MyKnee system, the AIKNEE system's accuracy requires further enhancement, and overall, its prosthesis prediction level is considered moderate.

Limitations

In this study, it is worth noting that one patient exhibited a flexion deformity of 15° and laxity in the medial collateral ligament, which resulted in the largest deviation in tibia and femur size, as well as the highest number of insertions. The failure to exclude factors that might have affected its accuracy was a weakness of this trial. Factors affecting accuracy include severe coronary deformity > 15°, knee stiffness and inadequate extension > 15°, flexion range < 90°, severe medial relaxation > 10°, or severe lateral relaxation > 15° ^[16]. Furthermore, the absence of cartilage visibility on CT scans may introduce bias in predicting osteotomy thickness, and the precision of preoperative planning may diminish in cases of trochlear dysplasia ^[31].

Abbreviations	Full name
TKA	total knee arthroplasty
mFTA	mechanical femorotibial angle
LDFA	lateral distal femoral angle
MPTA	medial proximal tibial angle
aFTA	anatomical femorotibial angle
AMA	anatomic mechanic axis deviation
PCA	posterior condylar axis
3D	three-dimensional
2D	two-dimensional
ROM	range of motion
VAS	visual analogue score
KSS	knee scoring system scores

Ethics Approval and Informed Consent

This study was approved by the Ethics Committee of The Second People's Hospital of Fujian Province (SPHFJP-K2022017-02), all patients have explained the treatment process in detail and signed the informed consent. All procedures performed on this study were in accordance with the ethical standards of the 1964 Helsinki declaration.

Consent for publication

Not applicable.

Availability of data and materials

All data are fully available without restriction. Requests for data can be made to the corresponding author.

Competing interests

The authors report no conflicts of interest in this work.

Funding

This work did not receive any funding.

Authors' contributions

Saijiao Lan is responsible for research design, data collection and manuscript generation. Jian Li is a senior author of the surgery and contributed to the design of the study and manuscript editing. All authors have read and approved the final manuscript.

Acknowledgements

Acknowledgements Our gratitude to prof. Acknowledgements My deepest gratitude goes first and foremost to Professor Li, my supervisor, for his constant encouragement and guidance. Acknowledgements We thank the patient who contributed to this report, as well as the staff of each department. Thanks to the Department of Sports Medicine in the Second People's Hospital of Fujian Province.

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No competing interests reported.

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You are reading this latest preprint version

Assessment of Preoperative Planning and Intraoperative Accuracy of the AIKNEE System for Total Knee Arthroplasty

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials and methods

Patients

AIKNEE Workflow

Knee-related Parameters

Data Collection

Statistical Analysis

Results

Discussion

Preoperative and postoperative functional changes

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1