Surface Characterisation of Retrieved Orthopaedic Knee Liners

Abstract

Total knee arthroplasty (TKA) is one of the most frequently performed surgical procedures for patients with advanced knee joint disease, which is intended to relieve pain and restore normal joint function. A critical component of the TKA system is the ultra-high-molecular-weight polyethylene knee liner, which acts as the bearing surface between the metallic components. Despite continuous improvements in material processing and implant design, these liners remain vulnerable to several damage mechanisms such as wear, fatigue, delamination, oxidative degradation, pitting, embedded debris, overload, creep, edge damage, backside wear, and fracture. This study introduces a new quadrant-based characterization system to evaluate retrieved knee liners through non-destructive methods. The liners, collected from revision surgeries, were divided into nine anatomical zones labelled Q1 to Q9 to systematically identify and map surface damage. Damage density was determined manually as well as by using computational image analysis through MATLAB R2024a and Python 3.13. The computational methods demonstrated greater accuracy and reproducibility, showing a strong correlation with manual evaluation, with p equalling 0.41 for Python and p equalling 1.00 for MATLAB. The proposed quadrant-based system, together with computational validation, offers a more reliable framework in studying wear and damage patterns in retrieved implants. This approach contributes to an enhanced understanding of how different damage modes interact and offers useful guidance for enhancing implant design, material durability, and clinical outcome improvement in total knee arthroplasty.

1. Introduction

Total knee arthroplasty (TKA) is a well-established surgical treatment for end-stage osteoarthritis and other degenerative knee disorders, providing effective pain relief and functional improvement [1]. The long-term success of TKA largely depends on the durability of the knee liner, commonly manufactured from ultra-high-molecular-weight polyethylene (UHMWPE), which functions as the primary bearing surface between the femoral and tibial components [2,3]. Despite advances in implant design and material processing, polyethylene liner degradation remains a major contributor to implant failure and revision surgery [4].

Polyethylene liners are subject to multiple damage mechanisms, including wear, delamination, oxidation, creep deformation, and fatigue cracking. Retrieval studies have identified characteristic surface features such as scratches, pitting, embedded debris, and fatigue striations, reflecting the combined influence of micromotion, third-body particles, and oxidative processes on in vivo degradation [5]. Similar damage patterns observed across different joint implants indicate that polymer wear is governed by common tribological principles [6]. Non-destructive analysis of retrieved liners is therefore essential for understanding in vivo wear behaviour and improving implant performance. However, many existing wear assessment methods rely on subjective visual scoring or global surface inspection, offering limited spatial resolution and poor reproducibility. These approaches often fail to localise damage to specific anatomical regions, potentially overlooking clinically relevant stress concentrations. To address these limitations, the present study introduces a quadrant-based surface characterization framework that systematically maps wear features across anatomically defined zones of retrieved knee liners. When combined with computational image analysis using MATLAB and Python, this approach reduces observer bias and enables a more objective, reproducible quantification of wear patterns.

Recent advances in smart knee implant technologies, including sensor-based monitoring of load and kinematics, further highlight the importance of spatially resolved wear analysis [7]. Integrating localised retrieval-based characterization with such data-driven approaches may improve understanding of polyethylene degradation mechanisms and support future advancements in implant design, surgical alignment, and long-term clinical outcomes [8].

2. Knee Liners

Knee liners are one of the few features that correspond to the joining bearing surface between the metal femoral and tibial components. The majority of liner designs are almost exclusively made up of ultrahigh-molecular-weight polyethylene (UHMWPE) or its advanced generations. They ensure smooth articulation by load distribution, shock dissipation, and stable loading conditions on the joint surfaces [9]. This overcomes abrasion and wear on the implant, ensuring proper functioning of the knee and durability. They are, however, subject to wear, cracking, or deformities over time, all eventually leading to clinical implant failure, though mechanically intact, thus becoming focal points in the studies of failure analysis and suggestions toward the improvement of implants [10].

2.1. Types of Knee Liners

As far as the prosthetic performance and the patient’s survivorship are concerned, such devices, or knee liners, are subdivided into fixed and mobile-bearing tibias.

2.1.1. Classification by Motion Type

(a)

Fixed-bearing liners: Fixed-bearing tibia liners like in Figure 1 left side ones are fixed to the tibial baseplate, providing stability of the tibial component with minimal movement. They are provided for knees where the ligaments are intact, and alignment is satisfactory.

(b)

Mobile-bearing liners: Mobile-bearing inserts allow for slight motion between the liner and the tibial component like shown in Figure 1 left side ones, supposedly to provide contact mechanics that mimic those of the normal knee. These are provided for knees that need more range of motion and less stress on the implant.

2.1.2. Constraint-Based Classification

Cruciate-retaining (CR) liner:

The CR liner is designed to keep the posterior cruciate ligament intact, allowing for more natural knee movement postoperatively. Its smooth, curved surface as seen in Figure 2, allows the knee to bend and straighten, like it naturally would, through the action of the PCL, which provides stability. This type is ideal for those patients in whom the ligaments are still strong and healthy, offering a more natural feel when one walks, climbs stairs, or does daily activities.

Posterior-stabilised (PS) liner:

The PS liner is utilised in case of damage or surgical removal of the PCL. Due to the absence of this ligament, a special mechanism of a post and cam is implemented in this liner; it works like a small peg which fits into a groove as seen in Figure 2, to keep the knee stable while moving. This controls the back-and-forth motion of the thigh bone over the shin bone, thus providing extra support in cases of moderate instability of the knee, while still allowing for smooth movement.

Constrained condylar knee-CCK liner:

The CCK liner is indicated for knees that require more stability than the PS liner can provide. It has a larger and more solid post with deeper grooves as seen in Figure 2, to resist sideward flexion, which are useful when there is some weakness or rupture of the medial or lateral collateral ligaments. This liner is often used in complicated knee surgeries, like revisions or cases with severe deformities, to help to stabilise the knee when the natural ligaments are not performing their job.

Hinged total knee replacement:

This is a heavy-duty alternative for knees that have lost nearly all their ligament support. The thigh and shin bones are joined together by an actual hinge system, like in a door hingeas seen in Figure 2, that allows the knee to flex and extend while avoiding slippage in undesired directions. This kind of implant is used in the most challenging cases—such as severe knee instability, major bone loss, or after multiple failed knee surgeries.

3. Materials Used in Knee Liners

Different materials are used in knee liners to make the joint move smoothly and last longer. Each material is chosen for its strength, wear resistance, and safety inside the body. The

Table 1. Materials and their properties used in knee liners.

Name	Material	Properties	Advantages
UHMWPE	Ultra-High Molecular Weight Polyethylene	High molecular weight polymer; extremely tough, low friction, high wear and impact resistance, biocompatible [13].	Provides smooth articulation, long-term durability, reduces wear particles in knee liners.
HCLPE	Highly Cross-Linked Polyethylene	Cross-linked through radiation; enhanced wear resistance, reduced oxidation, stable molecular network.	Minimises wear debris and osteolysis, extends implant lifespan, ideal for active patients.
Vitamin E Stabilised Polyethylene	UHMWPE Blended or Diffused With Vitamin E (antioxidant)	Excellent oxidative stability, maintains mechanical strength, resists ageing and fatigue.	Prevents long-term degradation, maintains implant integrity, suitable for younger or active patients.
PEEK	Polyether Ether Ketone	High-performance thermoplastic, lightweight, rigid, corrosion- and fatigue-resistant, radiolucent.	Strong yet light, withstands high loads, metal-free option with excellent biocompatibility.
Metal-Backed Polyethylene (Co-Cr)	Cobalt-Chromium Alloy + Polyethylene insert	Very high strength, hardness, corrosion resistance, stable under cyclic loading.	Provides rigid support to liner, evenly distributes joint load, enhances stability and wear performance.
Metal-Backed Polyethylene (Ti-6Al-4V)	Titanium Alloy (Ti-6Al-4V) + Polyethylene insert	Lightweight, strong, corrosion-resistant, excellent biocompatibility, elastic modulus closer to bone.	Offers durable yet lightweight support, reduces stress shielding, ideal for complex or revision implants.

below gives a summary of the most common materials used for knee liners, their key properties, and their main advantages in knee replacement systems.

4. Causes of Failure Due to Knee Liner

Failure in the knee liner within total knee arthroplasty may be due to several different causes, which are all related to poor implant performance and patient discomfort. Some of the most reported causes include the following.

4.1. Wear

Continuous mechanical stress results in degradation of the polyethylene material into wear particles, which may cause osteolysis and loosening of the implant [14].

4.2. Oxidative Degradation

The polyethylene can become oxidised due to long-term exposure to oxygen; this will embrittle it, potentially causing cracking or delamination and thus mechanical failure.

4.3. Mechanical Overload

Undue stress due to high-impact activities, obesity, or improper implant alignment can result in the liner deforming or fracturing beyond its material stress tolerance.

4.4. Design and Manufacturing Defects

Flaws in implant design or manufacturing processes, such as improper thickness or surface imperfections, introduce weaknesses that cause the liner to prematurely fail.

4.5. Improper Surgical Technique

Poor alignment of the components or inadequate fixation at the time of surgery can result in improper stress distribution on the liner, thereby accelerating wear and increasing the possibility of mechanical failure. Recent research has begun to consider the different anatomic and functional features of men and women through the specific gender identity lens for articulating the possibilities at play in these failure mechanisms.

4.5.1. Anatomical Differences Between Genders

Anatomical studies indicate that women generally have a less prominent anterior condyle, a greater quadriceps angle, and a narrower mediolateral-to-anteroposterior aspect ratio than men. These structural differences have led to the development of gender-specific knee implants that accommodate female anatomy. However, an in-depth review has identified that though such prosthetics decrease the risk for lateral femoral component overhang, they are not associated with any clinically significant advantages compared to the standard unisex designs.

4.5.2. Functional Differences and Clinical Outcomes

Functionally, women undergoing TKA report more disability, less quadricep strength, and greater preoperative pain than men, on average, which are factors that may impact recovery from surgery and the working of the implant. Thus, women were more likely to experience moderate-to-severe pain two years after primary TKA when age and baseline health conditions were accounted for. These differences may lead to indirect performance differences and wear and tear due to knee liner failures.

5. Common Failure Modes of Knee Liners in Total Knee Arthroplasty

These fail mechanisms affecting knee liners in total knee arthroplasty are of a mechanical, biological, and, mainly, design origin. Understanding and recognizing this failure mode will improve implant longevity and the greatest outcome for patients.

5.1. Abrasive Wear

Abrasive wear generally occurs with hard particles or rough surfaces passing over or against trouble-free surfaces, wearing away material. It is observed that, usually, the metallic portions of the implant roughen with time, thereby causing surface scrapes on the liner. This mechanism has the capability to open microcracks; as a result, these channels provide a further avenue for wear, eventually leading to poor liner thickness and very little functional integrity [15].

5.2. Adhesive Wear

Adhesive wear occurs when two surfaces that are in contact stick momentarily during motion and tear the polyethylene during separation. This mechanism is very common in high-contact stress situations, especially when there is limited lubrication between the implant components.

5.3. Fatigue Cracking

Fatigue cracking can set in after some time, where repeated mechanical loading induces the formation of small cracks in polyethylene. When stress continues, the cracks may propagate to the liner, in turn severely compromising its structural integrity [16].

5.4. Delamination

Delamination can refer to the separation of some layers in the polyethylene liner; it may be due to subsurface fatigue failure. The condition represents a weakening that might allow for further cracking and fragmentation of the liner, especially within high-stress regions of the implant.

5.5. Oxidative Degradation

Oxidative degradation is the process whereby polyethylene reacts with oxygen over an extended period, leading to embrittlement and dulling of the mechanical properties. Degradation can also occur during the sterilization process or after implantation, thus increasing the rate of failure and wear [17].

5.6. Pitting and Embedded Debris

Pitting is the state when small depressions are formed on the liner surface, usually due to the activity of a third-body particle such as fragments from bone cement or metal debris. Such particles become embedded in the liner, act as abrasives, and accelerate local wear [18].

5.7. Creep Deformation

Creep deformation is the slow, time-dependent plastic deformation of polyethylene under constant stress. This permanent change may affect the thickness of the liner, affect joint mechanics, and add increased risk for instability [19].

5.8. Rim Loading and Rim Damage

The forces are concentrated toward the periphery of the liner, rather than being evenly distributed over the entire surface; this may result from malalignment or instability, leading to rim cracking and chipping, especially in thin liners [20].

5.9. Fracture

Complete fractures can occur, albeit rarely, in the polyethylene liner due to a combination of material fatigue, oxidative degradation, and exceptionally high mechanical stress. Fractures have ramifications on the stability of the implant and universally require revision surgery [21].

5.10. Backside Wear

Backside wear is the wear found on the non-articulating surface of the liner, where the liner has contacted the tibial baseplate. Micromotion at this interface, especially in modular implants, can lead to fretting and the generation of wear debris, which in turn allow for the implant to loosen over time [22].

6. Failure of the Knee Liner in Total Knee Arthroplasty

Knee liner failure impacts patient outcomes, implant performance, and healthcare resources. The most important effects are highlighted below.

6.1. Clinical Implications

Liner wear is typically first manifested by persistent pain that is secondary to the inflammation and irritation of the joint. Further deterioration of the liner may result in reduced mobility, functional difficulties with activities of daily living, and a decline in functional scores compared to primary TKA. Loss of joint congruency results in instability, subluxation, or dislocation, and most cases eventually require complex revision surgery with higher complication rates.

6.2. Mechanical and Structural Effects

Wear particles can induce osteolysis, reducing the strength of the surrounding bone, which could lead to implant loosening. Complete liner failure may result in metal-to-metal contact and accelerate component wear. Malalignment at the time of the initial surgery also leads to increased stress on the liner, accelerating degradation.

6.3. Biological Effects

For this reason, polyethylene debris may trigger an immune response that causes progressive bone loss around an implant. If severe wear exposes metal surfaces, the resulting metallosis may cause tissue damage, pain, and further instability.

6.4. Psychological and Emotional Impacts

Anxiety and depression can occur due to chronic pain and reduced independence. Activity limitations and social withdrawal reduce quality of life even more.

6.5. Socioeconomic Impacts

Revision procedures are much more expensive than primary TKAs and involve longer recoveries. Patients miss work or are less productive, and as revision cases continue to climb, so do demands on health resources.

6.6. Impact on Surgical Practice and Implant Design

The surgical techniques have been refined by experience with liner failures, with emphasis on accurate alignment and soft tissue balancing. Improvements in polyethylene processing, particularly with highly cross-linked materials, have reduced wear. Follow-up and imaging on a regular basis are still important for the prevention of major complications.

7. Experimental Failure Analysis of Knee Liners

7.1. Materials and Methods

7.1.1. Sample Collection and Ethical Considerations

Unknown retrieved knee liner samples were obtained from revision total knee arthroplasty procedures through collaboration with orthopaedic surgeons. All samples were fully de-identified prior to analysis, and no patient-identifiable information was available at any stage of the study. As the investigation involved analysis of discarded medical devices without access to protected health information, institutional review board (IRB) approval and patient consent were not required, in accordance with institutional guidelines.

The samples represent failed knee liners retrieved during revision surgeries and reflect real-world clinical failure conditions. Limited de-identified information, including the implant design type and general reason for revision, was available. Detailed patient demographics such as age, body mass index (BMI), activity level, implantation duration, and surgical alignment parameters were not accessible and are acknowledged as a limitation of this study.

7.1.2. Preparation of Samples for Investigation and Imaging Protocol

The knee liner samples were neatly placed on a clean sheet of paper to avoid any contamination. Each sample was individually labelled—KL1, KL2, KL3, KL4, and KL5—for identification and easy reference for documentation purposes during the analytical processes. The knee liner samples were placed on a clean, neutral background to avoid contamination and to improve contrast during imaging. Each sample was individually labelled as KL1, KL2, KL3, KL4, or KL5 to ensure consistent identification throughout the analytical process.

High-resolution images of each sample were captured using an iPhone 14 Pro camera (>12 megapixels) to document the surface features and visible defects. Imaging was performed under standardised conditions, including a fixed camera-to-sample distance, consistent overhead lighting, and a neutral background to minimise glare and shadowing. A digital magnification application (10×) was used to obtain close-up images, enhancing the visualization of fine wear patterns, micro-cracks, pitting, and other surface anomalies without physical contact with or alteration of the samples.

Following labelling and imaging, the partition system shown in Figure 3 was applied to each sample. This system divides the UHMWPE knee liner into nine anatomically defined zones for systematic evaluation of wear patterns, surface damage, and deterioration modes. The extent of damage in each region is referred to as Q1 through to Q9 in the description and analysis sections, enabling consistent localization and comparison of damage features across samples.

7.2. Observation and Comparative Analysis from the Literature

The retrieved polyethylene knee liner components are presented in Figure 4. A total of five knee liners (KL-01 to KL-05) were collected following revision knee arthroplasty and analyzed to represent a range of commonly used liner designs. The retrieved samples comprised cruciate-retaining (CR), posterior-stabilized (PS), fixed-bearing, and constrained liner configurations, allowing comparative evaluation of surface features across different implant geometries. All components were visually inspected and documented all observations as presented in

Table 2. Visual observations at first instance.

Knee Liner Sample (Figure 4)	Identified Liner Type	Visual Observations
KL-01 (Figure 4a)	Cruciate-Retaining (CR) Liner	Some surface wear and slight material thinning seen [23].
KL-02 (Figure 4b)	Posterior-Stabilised (PS) Liner	Scratches and minor edge damage observed [24].
KL-03 (Figure 4c)	Fixed-Bearing Liner	Surface wear and slight shape change due to pressure [25].
KL-04 (Figure 4d)	Posterior-Stabilised (PS) Liner	Cracks in the central post and some wear due to friction [26].
KL-05 (Figure 4e)	Constrained Liner (Circular Component)	Wear around screw holes, material ageing, circular liner shows surface scratches and central wear marks [27].

.

The macrophotographic images captured are collated in Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 of the retrieved UHMWPE knee liners KL-01 to KL-05. They were captured using a mobile phone camera with 10× digital magnification. These images provide an initial visual overview of the surface condition of each liner prior to detailed quadrant-based and quantitative analysis. The magnified views highlight gross wear features such as scratches, pits, edge deformation, discoloration, cracking, and localized material loss that are indicative of in vivo mechanical loading, oxidative degradation, and fatigue-related damage. Together, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 establish a visual baseline for comparing surface damage characteristics across different liner designs and constraint levels and serve as a foundation for subsequent qualitative and quantitative evaluation of wear mechanisms described in the following sections.

The detailed failure analysis of the five retrieved UHMWPE knee liners (KL-01 to KL-05) by correlating macroscopic surface observations with dominant failure modes and their anatomical distribution are presented in

Table 3. Detailed analysis of each liner.

Liner ID	Primary Observation(s)	Dominant Failure Mode(s)	Affected Zones	Validated Mechanism/Literature Insight
KL-01	Polished surface; edge deformation; discolouration at posterior; micro-cracks on posterior post	Surface wear, oxidative degradation, fatigue cracking, delamination	Q1–Q3, Q4–Q9, Q8 (posterior post)	Polishing and oxidative wear are common in long-term UHMWPE use, posterior post fatigue from repetitive impingement stresses [28].
KL-02	Edge material loss; scratches on contact surface; posterior post fracture; surface delamination	Edge damage, adhesive/abrasive wear, fatigue fracture, third-body wear [29]	Q4–Q9, Q8 (posterior post)	Excessive loading and third-body debris accelerate wear; posterior post fractures from cyclic impingement [30].
KL-03	Discolouration; scratches with debris; minor creep deformation; rounded edges	Oxidative degradation, abrasive wear, early creep, fatigue wear	Q1–Q3, Q4–Q9	Oxidation and mechanical fatigue lower polyethylene strength; long-term compressive stress causes creep and surface softening [31].
KL-04	Posterior post fracture; cracks propagating into tibial surface; oxidation and embedded debris	Fatigue cracking, oxidative degradation, third body and adhesive wear [32]	Q1–Q3, Q4–Q9, Q8 (posterior post)	Posterior-stabilised designs prone to cyclic stress cracking and oxidation; debris accelerates surface fatigue.
KL-05	Polished surface; wear near fixation holes; embedded debris; screw deformation and corrosion	Surface wear, third-body wear, fatigue at fixation, screw failure	Q1–Q9, Q8 (fixation), metallic components	High stress at screw holes promotes localised fatigue and corrosion; constrained liners limit motion, increasing edge wear and screw failure risk.

. It integrates visual inspection findings with the quadrant-based mapping system (Q1–Q9) as in Figure 3, to identify the primary wear and damage mechanisms affecting each liner. By linking observed surface features to validated failure mechanisms reported in the literature, Table 3 provides a structured comparison of damage severity, localisation, and probable causes across different liner designs and constraint levels, forming the basis for subsequent quantitative and microscopic evaluation.

8. Microscopic and Quantitative Study of Wear-Induced Failure in Knee Implant Liners

Five ultra-high molecular weight polyethylene knee liners, KL-1 through to KL-5, were retrieved in total knee arthroplasty revision surgeries. The liners were already implanted in patients and were being taken out due to complications or failures that led to revision surgery. The aim of this research was to assess the degree of surface damage in each of these liners by using visual and microscopic techniques. The signs of damage were scratches, pits, and delamination marks, all of which were leading to implant failure in the long run [33]. Such analysis provided an overview of the mechanical and material degradation of the knee liners following implantation. The dimensions noted are represented in

Table 4. Dimensions of knee liners.

Knee Liner	Length (cm) Average	Width (cm) Average	Thickness (cm) Average
KL-01	6.5	2	1
KL-02	5.5	3	1.1
KL-03	6.5	4	1
KL-04	5.5	3	1.2
KL-05	7.3	7.5	0.3

.

The liners varied in the extent of their wear. KL-1 and KL-2 had moderate wear with visible scratches and small pits in several zones. KL-3 had minimal wear, with slight polishing and barely any visible defects, suggesting that it experienced minimal mechanical stress or was better aligned. KL-4 and KL-5 were heavily damaged at the surface with deep scratches, delamination, and heavy pitting [34]. Microscopic photography on KL-1 and KL-2 also identified the presence of scratches and abrasions, based on naked-eye observations. Such variations necessitated special analysis to analyse the failure mechanisms.

The quantitative distribution of surface damage features across the nine predefined anatomical quadrants (Q1–Q9) for each retrieved knee liner is performed and noted in

Table 5. Quantitative damage counts by quadrant.

Sample	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Total Scratches	Total Pits	Total Defects
KL-1	1	1	2	1	2	2	0	1	1	11	7	18
KL-2	1	2	1	1	2	1	0	1	2	9	6	15
KL-3	0	0	1	1	0	0	0	0	1	3	2	5
KL-4	2	3	2	2	3	4	1	3	3	17	13	30
KL-5	3	2	2	3	5	5	2	4	3	15	18	33

Q1–Q9 are the nine anatomical zones in the partition system used for TKA liner wear analysis. Total scratches and pits are manual totals across quadrants per sample. The counts are surface damage features that were observed by optical microscopy and gross inspection which is collated in Figure 10 and Figure 11. The Figure 10 and Figure 11 highlight key wear features including scratches, pits, delamination, and embedded debris identified during visual inspection and quadrant-based analysis. These microscopic observations provide further confirmation of the dominant failure modes and support the quantitative damage trends reported in Table 5.

. It reports the total counts of scratches, pits, and overall defects, enabling direct comparison of damage extent and spatial localisation among the five samples. This quantitative assessment supports objective evaluation of wear severity and complements the visual and mechanistic analysis presented earlier.

Figure 10. Defects observed under digital and optical microscope.

Figure 10. Defects observed under digital and optical microscope.

Figure 11. Defects observed under digital and optical microscope.

Figure 11. Defects observed under digital and optical microscope.

A qualitative classification of surface damage features observed across the five retrieved UHMWPE knee liners is presented in

Table 6. Qualitative damage counts by type.

Sample	Scratches Count	Pits Count	Deep Scratches (>0.5 cm)	Pit Clusters (>3)
KL-1	11	7	3	1
KL-2	9	6	2	1
KL-3	3	2	0	0
KL-4	17	13	7	3
KL-5	15	18	6	4

KL-04 and KL-05 exhibited the highest severity of surface damage as per Table 6 data, with greater numbers of deep scratches and clustered pits, indicating advanced wear and fatigue-related degradation. In contrast, KL-03 showed minimal damage severity, with no deep scratches or pit clusters, suggesting comparatively lower mechanical stress and better surface integrity.

, focusing on the relative severity of scratches and pits. In addition to total counts, the table identifies the presence of deep scratches and clustered pits, which are indicative of advanced wear and fatigue-related damage. This qualitative assessment complements the quantitative defect counts by highlighting differences in damage intensity and complexity among the liners, thereby offering further insight into the progression of wear mechanisms associated with implant design and in vivo loading conditions.

9. Analysis of the Data

The distribution of scratches and pits across the nine anatomical quadrants (Q1–Q9) for all retrieved knee liners is represented in Figure 12 as a box plot, highlighting clear variations in damage localisation and severity among samples. Liners KL-04 and KL-05 demonstrate consistently higher defect counts across multiple quadrants, indicating extensive surface deterioration and non-uniform load distribution. In contrast, KL-03 exhibits minimal defect counts across most quadrants, suggesting favourable alignment or reduced mechanical demand. Figure 13 presents the mean defect values and corresponding statistical analysis obtained using JMP, providing a comparative overview of wear severity across liners. The observed trends confirm that liners with higher defect densities also exhibit greater variability in damage, reinforcing the relationship between surface wear accumulation and increased risk of mechanical failure.

To standardise comparison of wear between the five samples, the wear severity index (WSI) was calculated and reported in

Table 7. Wear severity index (WSI).

Sample	Total Defects (Scratches + Pits)	WSI (%)
KL-3	5	15.20%
KL-2	15	45.50%
KL-1	18	54.50%
KL-4	30	90.90%
KL-5	33	100.00%

. The formula used was as follows:

WSI = (((Pits Count + Scratches Count)/Maximum (Total Pits + Scratches))) × 100

This standardised measure allowed for ranking of each liner according to the relative degree of surface damage (Figure 14). KL-05 recorded the highest WSI at 100% and was closely followed by KL-04, which registered the second highest WSI; thus, they experienced the highest wear. KL-03 contained a much smaller WSI and was determined to be the least worn sample. Supporting WSI counts were validated with visual data plotted in JMP Pro 18 as defect distribution by quadrant.

The WSI is intended as a relative comparative index for ranking damage severity across retrieved liners, rather than as an absolute clinical threshold, particularly given the limited sample size and absence of outcome-based calibration.

The scratch and pit densities were calculated by first counting the total number of scratches and pits in each knee liner quadrant (Q1 to Q9), and numbers obtained were reported in

Table 8. Count of scratches, pits, and their density.

Liners	Total Scratches	Total Pits	Total Defects (Scratches + Pits)	Scratch Density	Pit Density
KL-1	11	7	18	11/18 = 0.61	7/18 = 0.39
KL-2	9	6	15	9/15 = 0.60	6/15 = 0.40
KL-3	3	2	5	3/5 = 0.60	2/5 = 0.40
KL-4	17	13	30	17/30 = 0.57	13/30 = 0.43
KL-5	15	18	33	15/33 = 0.45	18/33 = 0.55

. Tndividual number of stratches and pits are presented in graphical form in Figure 15. The total scratches and total pits for each sample were also added in Table 8.

To determine the scratch density, the total scratches for each sample were divided by the total number of defects (scratches + pits). Similarly, the pit density was determined by dividing the total number of pits by the total number of defects per sample. This provides the ratio of the quantity of surface damage in the form of scratches to pits, which is useful in forming an understanding of the wear pattern and failure mechanism of the knee liners.

This quantitative and microscopic failure analysis had important data regarding the wear of UHMWPE knee liners during TKA, the details for each liner is noted in

Table 9. Microscopic observation details for each liner.

Sample	Liner Type	Macroscopic Observations	Microscopic Surface Characteristics	Pit Severity	Scratches Severity
KL-01	Cruciate-Retaining (CR) Liner	Some surface wear, slight material thinning seen, minor irregularities due to machining	Smooth surface with minor machining marks	Low	Low
KL-02	Posterior-Stabilised (PS) Liner	Scratches, minor edge damage, early-stage delamination and minor chipping near lateral contact area	Slightly rough, visible polymer grain texture, surface irregularities, rougher than KL-01	Medium	Medium
KL-03	Fixed-Bearing Liner	Surface wear and slight shape change due to pressure, consistent finish	Uniform texture with fine machining marks	Low	Low
KL-04	Posterior-Stabilised (PS) Liner	Cracks in central post, wear due to friction, significant delamination and severe chipping in high-stress areas	Rougher texture, inconsistent polishing, deep scratches, tool marks, rough internal edges	High	High
KL-05	Constrained Liner (Circular Component)	Wear around screw holes, material ageing, scratches, localised delamination and minor chipping near fixation areas	Scratches, tool marks near drilled holes, burrs near drilled edges, slight oxidation marks	Medium	High

. The liners KL-04 and KL-05, being the most worn, experienced focal damage in stressed areas, possibly suggesting that cyclic loading, inappropriate fixation, or implant misalignment were contributory to their damage. Conversely, KL-03, which had the least damage, had the benefit of superior material performance or surgical alignment. These findings highlight the importance of material longevity, ideal implant design, and surgical precision. Collectively, quadrant analysis, digital microscopy, and severity indexing provide an integrated platform for orthopaedic implant performance assessment and subsequent improvement.

While the research provides important information regarding surface wear and failure modes of UHMWPE knee liners in TKA surgery, there are certain limitations of the research that need to be mentioned. First to be noted is that five knee liners (KL-1 to KL-5) limited the sample, and they may not represent the entire spectrum of failure modes or of the different types of implants. In addition, patient-related variables such as age, activity level, implant age, body mass index (BMI), and surgical alignment information were unavailable, limiting the correlation of wear patterns with clinical outcomes [35]. Only visual and microscopic surface analysis were performed in the study; no mechanical tests such as hardness, tensile strength, or fatigue resistance were performed. In addition, imaging was limited to 20× magnification and possibly lost nano-scale wear features or incipient-stage oxidation effects [36]. Although quadrant-based damage mapping is used, scratches and pits are subjectively interpreted with variability. Multi-modal analysis (mechanical and chemical), population studies of larger numbers, and patient metadata would enable further inclusive knowledge of implant performance and failure risk in the future.

10. Comparison with Tool Analysis

The analysis of the data integrates manual and automated approaches to evaluate wear patterns, damage distribution, and severity across the retrieved UHMWPE knee liners.

Table 10. Manual vs. tool-based counts.

Knee Liner	Manual Scratch Count	Tool-Based Scratch Count	Manual WSI (%)	Tool-Based WSI (%)	Manual Severity	Tool-Based Severity
KL-1	11	13	64.71%	72.22%	Moderate	Moderate
KL-2	9	9	52.94%	50.00%	Moderate	Moderate
KL-3	3	5	17.65%	27.78%	Mild	Mild
KL-4	17	18	100.00%	100.00%	Severe	Severe
KL-5	15	16	88.24%	88.89%	Severe	Severe

provides a direct comparison of defect counts and wear severity indices obtained from manual assessment and tool-based analyses, establishing the level of agreement between methods. Figure 16 illustrates the overall distribution of all detected defects across the liners, while Figure 17 presents a quadrant-wise representation of scratch counts highlighting spatial variations in surface damage.

The comparison between manual and tool-based scratch counts is illustrated in Figure 18, demonstrating close agreement between methods with minor variations across the retrieved knee liners.

A combined graphical representation of wear metrics is shown in Figure 19, in which the teal line represents the manual scratch count, the orange line indicates the tool-based scratch count, the purple line corresponds to the manual wear severity index (WSI), and the green line represents the tool-based WSI. In addition, the red line denotes manual severity classification, while the blue line represents tool-based severity classification, enabling direct comparison of quantitative counts and qualitative severity trends across liners.

A comparative summary of scratch counts obtained through manual assessment and automated analysis using Python and MATLAB is provided in

Table 11. Counts as per manual, Python, and MATLAB calculations.

Knee Liners	Manual Scratch Count	Python Scratches Count	MATLAB Scratch Count
KL-01	11	6	7
KL-02	9	12	10
KL-03	3	10	6
KL-04	17	20	18
KL-05	15	16	14

, allowing direct evaluation of agreement and variability between methods across the retrieved knee liners. The results highlight differences in detection sensitivity and consistency among the analytical approaches, supporting the reliability of computational methods as effective alternatives to manual counting for surface wear assessment.

Manual Counting:

Manual counting requires visual observation of the knee liners and manual counting of the observed scratches and pits in the images. It is heavily based on an observer’s attention and consistency; hence, this technique may have variability and human error. This approach is very basic, inexpensive, and reasonable for small data sets; however, it is extremely time-consuming and, therefore, is not practical to handle a large quantity of images. The major advantage is the potential to conduct a direct, simple visual observation; this may affect the precision and reproducibility of the results, owing to the subjective characteristic of the method.

MATLAB Analysis:

MATLAB offers an excellent environment both for image processing and data analysis, allowing one to perform automated detection for both scratches and pits by using specific toolboxes. It uses a variety of algorithms, including thresholding, edge detection, and feature extraction, to observe wear patterns in knee liners. MATLAB efficiently processes large volumes of data with uniform and reproducible results. While it requires expertise in programming to set up and fine-tune analysis, it is highly regarded for its excellent performance and precision, especially when handling challenging image-processing applications.

Python Analysis:

Python 3.13 gives the best flexibility in processing images and data analysis, using libraries such as OpenCV for feature identification. The positive aspects of using Python are that it is an open-source language that is easily integrated with other tasks that involve data analysis. The language itself has a number of tools available for performing the automation of wear pattern detection in knee liners, which may be used and modified for specific needs. Python is highly scalable and able to handle large amounts of data efficiently, but it does require knowledge of several libraries and tends to execute code more slowly than MATLAB unless optimised.

Comparison:

Both MATLAB and Python have automated solutions for wear analysis, which makes them much faster, more consistent, and more scalable than manual counting. MATLAB is very useful for users in professions that require the processing of images to be performed with great precision. It is usually preferred in most academic and industrial circles, due to its specialised toolboxes. On the other hand, Python presents the user with an open-source option that can easily be adapted for a range of analyses. Its choice therefore seems to depend on the preference, budget, and specific needs of the user. As much as MATLAB might be an ideal solution for those who have access to its resources, Python is a cost-effective alternative that offers versatility in analysis pipelines.

The comparison of wear assessment methods is further illustrated through graphical and statistical evaluation. Figure 20 and Figure 21 visualise differences and similarities in scratch counts obtained from manual assessment and automated analyses, highlighting overall trends and method-dependent variability across the retrieved knee liners. These visual comparisons are supported by the statistical analysis presented in

Table 12. Comparison analysis using manual vs. Python and MATLAB.

Analysis Type	Manual vs. Python	Manual vs. MATLAB
Pearson Correlation	0.68	0.88
Paired t-test (t-statistic)	−0.92	0
Paired t-test (p-value)	0.41	1

, which quantifies the level of agreement between manual, Python-, and MATLAB-based methods using correlation and paired statistical testing. Together, these results demonstrate that automated tools show strong concordance with manual evaluation, particularly for MATLAB, reinforcing the reliability of computational approaches for wear quantification.

The analysis involves comparing manual wear data with data from Python and MATLAB, using the Pearson correlation and paired t-test. Below is an explanation of the values and what they indicate.

1.

Pearson Correlation:

The Pearson correlation measures the strength and direction of the linear relationship between two datasets. The values range from −1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no correlation.

Manual vs. Python: Pearson Correlation = 0.68

This suggests a moderate positive correlation between the manual counts and the Python-generated counts. A correlation of 0.68 indicates that while there is a general relationship between the two methods, there are still some differences that might be due to the subjectivity in manual counting or the algorithmic differences in Python.

Manual vs. MATLAB: Pearson Correlation = 0.88

The higher Pearson correlation of 0.88 between the manual counts and MATLAB-generated counts indicates a strong positive correlation. This suggests that MATLAB’s automated counting is closely aligned with the manual counting results, possibly due to the more defined image-processing techniques in MATLAB compared to Python.

2.

Paired t-test (t-statistic):

The paired t-test compares the means of two related groups to determine whether there is a statistically significant difference between them. The t-statistic reflects how much the two datasets differ, normalised by the standard error of the differences between them.

Manual vs. Python: t-statistic = −0.92

A t-statistic of −0.92 indicates a small difference between the manual and Python counts. Since the t-statistic is close to 0, it suggests that the difference between these two methods is not large, meaning the wear counts from both methods are quite similar, but not identical.

Manual vs. MATLAB: t-statistic = 0

The t-statistic of 0 means that there is no difference between the manual and MATLAB counts. This suggests that the counts from the manual method and the MATLAB method are very similar, showing that MATLAB’s automated analysis closely mirrors the manual observations.

3.

Paired t-test (p-value):

The p-value helps to determine whether the observed differences are statistically significant. A p-value less than 0.05 typically indicates a statistically significant difference.

Manual vs. Python: p-value = 0.41

A p-value of 0.41 means that there is no statistically significant difference between the manual and Python counts (Figure 22). This value is greater than 0.05, indicating that the difference between the two methods is not likely to be due to chance, but it still does not suggest a highly significant distinction.

Manual vs. MATLAB: p-value = 1

A p-value of 1 suggests that there is no difference between the manual and MATLAB-generated counts (Figure 22). This further confirms that the wear counts produced by MATLAB are very similar to those produced manually, making MATLAB a highly reliable tool for this analysis.

11. Discussion

The wear behaviour of the five retrieved knee liners (KL-1 through KL-5) was evaluated by using both raw and filtered datasets, providing insight into how polyethylene liners respond to varying mechanical stresses and loading conditions over time. Differences in wear magnitude and distribution across the liners reflect the combined influence of the alignment, loading history, and implant usage. KL-1 demonstrated a moderate variability in wear without a pronounced peak, suggesting relatively steady loading conditions with gradual stress accumulation [37]. This wear pattern is consistent with a reasonably well-functioning implant under typical daily activities; however, the progressive nature of the wear indicates that continued exposure to mechanical stress may eventually compromise the liner’s integrity.

Clinically, such gradual degradation may manifest as slowly increasing pain or reduced joint comfort over time. KL-2 exhibited a more pronounced wear pattern when compared to KL-1, indicating exposure to higher stresses or increased loading [38]. This may be related to elevated activity levels or suboptimal alignment, both of which are known to accelerate polyethylene wear. Clinically, increased wear of this nature has been associated with a higher risk of instability, inflammation due to debris generation, and earlier functional decline if not addressed. KL-3 showed minimal wear with a smooth and stable damage profile, suggesting either optimal implant alignment or reduced mechanical demand. This liner’s wear characteristics indicate favourable load distribution and material performance, which may translate clinically to improved joint stability, reduced pain, and enhanced long-term durability. Such patterns are consistent with a lower revision risk and better functional outcomes. In contrast, KL-4 demonstrated the highest wear variability and severity, which is indicative of high-stress loading, possible malalignment, or excessive mechanical demand [39]. Severe localised wear in such cases is clinically relevant, as it may contribute to joint instability, increased polyethylene debris generation, and accelerated osteolysis, ultimately increasing the likelihood of early implant failure and revision surgery. KL-5 displayed moderate wear with less fluctuation than KL-4, suggesting more uniformly distributed stresses overall, though localised damage was still evident in high-stress regions [40].

Clinically, this pattern may correspond to acceptable short-term function but increased long-term risk for focal damage-related complications if mechanical conditions persist. Overall, the findings demonstrate that knee liner wear is strongly influenced by mechanical factors such as stress magnitude, load distribution, and alignment, in addition to the intrinsic properties of the polyethylene material [41]. Localised wear patterns identified through quadrant-based analysis highlight regions that may be more susceptible to damage and clinically relevant failure mechanisms. These observations emphasise the importance of precise surgical alignment, appropriate implant selection, and balanced load transfer to ensure long-term implant durability and favourable patient outcomes.

11.1. Study Limitations

This study has several limitations. The small sample size (n = 5) limits statistical power and restricts the generalizability of the findings to broader patient populations and implant designs. Additionally, mechanical testing and chemical characterization of the liners were not performed; therefore, the conclusions are based on surface-level damage assessment, rather than direct measurements of material properties such as hardness, oxidation, or fatigue resistance. The absence of detailed patient-specific data, including activity level, body mass index, implantation duration, and clinical outcome scores, further limits the direct correlation between wear patterns and patient outcomes.

11.2. Future Directions

Future studies should apply the proposed quadrant-based wear assessment framework to larger, multi-centre retrieval cohorts to improve statistical robustness and enable standardised comparisons across implant designs and clinical settings. Integration of this spatial wear-mapping approach with emerging sensor-embedded smart knee implants offers significant potential. Combining in vivo measurements of loading, alignment, and joint kinematics with post-retrieval wear localization could enable direct correlation between mechanical exposure and damage progression, supporting improved implant design, patient-specific risk assessment, and long-term performance monitoring.

12. Conclusions

The study introduced an eight-quadrant mapping system across the medial and lateral regions of the retrieved knee liners to evaluate localised surface damage. Each quadrant was analysed for scratches, pits, burnishing marks, cracks, and delamination, and the total counts were used to determine the damage density for each liner. The results showed clear differences among the five retrieved liners. KL3 had the lowest number of surface defects, with only five total scratches and pits, representing a wear severity index of about 15 percent. KL1 and KL2 showed moderate damage, with 18 and 15 total defects, respectively, corresponding to mid-range severity between 45 and 55 percent. KL4 and KL5 recorded the highest levels of surface damage, with 30 and 33 total defects, giving them wear-severity indices close to 90 and 100 percent. These data indicate that scratches were the most frequent form of damage, followed by pits and burnishing marks, which became more pronounced in the highly stressed zones. The overall pattern revealed that as the number of scratches and pits increased, the damage density also increased, showing a clear correlation between surface deterioration and risk of mechanical failure. The findings demonstrate that differences in alignment, loading, and material response directly influence the extent of surface wear. KL1 and KL5 exhibited moderate wear and good functional performance, while KL3 was the least affected and was likely well-aligned. KL4 experienced the greatest deterioration, suggesting a higher stress concentration and malalignment. As the damage density increased, the likelihood of cracking, delamination, and structural failure also rose. These results emphasise the importance of precise implant alignment, proper load distribution, and careful material selection to improve the durability of knee liners. Ongoing patient monitoring and refinement in surgical technique remain essential for reducing wear and extending the lifespan of total knee arthroplasty components.