Abstract
Objectives
Patient-specific 3D-printed jigs improve surgical outcomes, yet their use in high tibial osteotomy (HTO) lacks widespread acceptance due to cost-related scepticism and workflow adaptation challenges. This work aims to facilitate the adoption of 3D printed patient-specific instrumentation by demonstrating the precision of jigs produced using affordable resin 3D printing.
Methods
Full-length tibial CT scans were used for 3D modelling, virtual HTO planning and designing of patient-specific jigs. The jigs were 3D printed using a ∼$550 resin printer, whereas the bones were printed in a ∼$600 filament printer. Achieved vs. planned corrections were compared using the 3D scanning superimposition method. Accuracy was assessed with paired t-tests, Bland-Altman plots, linear regression, and two one-sided t-tests (TOST).
Results
For Medial Proximal Tibial Angle (MPTA), the mean error was −0.05° ± 1.32° with no systematic bias (p=0.912), whereas for Posterior Proximal Tibial Angle (PPTA), it was 0.57° ± 0.38°, having a significant over-correction (p=0.004). Strong to excellent correlations were observed (R2: 0.77 for MPTA, 0.99 for PPTA). Corrections were equivalent within ±1° (TOST: p=0.042 and p=0.007).
Conclusions
Affordable 3D-printed jigs could achieve acceptable corrections in a preclinical simulation setting, offering cost-effective preoperative planning and surgical training.
Introduction
High Tibial Osteotomy (HTO) is a key surgery in orthopaedics, designed to correct knee misalignment, particularly in patients with medial compartment osteoarthritis [1]. By realigning the tibia, HTO redistributes weight-bearing forces across the knee joint, offering a conservative yet effective means to alleviate pain, improve function, and delay the need for total knee arthroplasty [2], 3]. Traditionally, HTO relies on preoperative planning with two-dimensional radiographs and intraoperative adjustments guided by tools like fluoroscopy. While effective, these conventional methods can introduce variability, prolong operative time, and expose patients and surgical teams to radiation. The recent focus on patient-specific instrumentation (PSI) aided by CT/MRI, software-based virtual planning, and 3D printing, promises a paradigm shift, offering greater precision in surgical cuts and alignments by tailoring interventions to individual patient anatomy [4], [5], [6], [7]. Barring a few exceptions, most of the studies have shown that use of PSIs can reduce errors, enhance outcomes, and streamline procedures, yet their adoption remains limited [8], 9]. In conventional HTO, a significant difference was observed in planned and achieved medial proximal tibial angle (MPTA) for a group of 30 patients (94.3° ± 2.4°), while the difference was insignificant in the same number of patients operated using PSI (94.1° ± 2.6°) [10]. Similarly, Hiranaka et al. [11], observed a small overall error of 0.2° ± 1.5° in open-wedge HTO using PSI.
The reluctance to embrace this technology stems primarily from two interrelated challenges: (i) the high cost of equipment and materials [12], 13] and (ii) the steep learning curve associated with transitioning from familiar techniques to a novel, technology-driven approach [14]. High-end 3D printers can cost thousands of dollars, placing them out of reach for many institutions, especially in smaller hospitals or developing regions. Moreover, surgeons accustomed to conventional methods may hesitate to invest the time required to master PSI, particularly without assurance of its superiority in practice [1], 15]. Despite these barriers, the evidence supporting improved surgical outcomes with 3D-printed jigs is compelling, suggesting that broader acceptance could transform orthopaedic care.
To bridge this gap, a cost-effective and accessible solution is essential – one that delivers precision, facilitates a gradual transition, builds surgeon confidence, and supports training without overwhelming resources. As such, this study proposes a solution by exploring the use of affordable 3D printers – priced under $1,000 – to produce patient-specific jigs for HTO. Leveraging resin and extrusion-based printing technologies, we achieve Bland-Altman limits of agreement within 3°, the commonly reported planned vs. achieved threshold in HTO [16], 17]. Preliminary results encourage the use of the present cost-effective 3D printing methods in preoperative planning and surgical education.
Materials and methods
Study design
This preclinical investigation was structured to evaluate the feasibility of affordable 3D printing for HTO (Figure 1). The primary outcomes of this proof-of-concept study were corrections achieved in MPTA and PPTA with respect to the virtual plans, making them paired continuous measurements. Therefore, sample size estimation was based on the precision of the 95 % confidence interval (CI) of the mean paired difference [18]. For paired data, the 95 % CI sample size can be calculated as:
where, SD is the expected standard deviation of the paired difference, and t is the critical value of the Student’s t-distribution. Using an SD of 1.5° reported by Hiranaka et al. [11] for MPTA measurements in PSI-guided HTO, and specifying a desired precision (half-width) of ±1.25°, the sample size was estimated using the large-sample critical value t=1.96:

Schematic chart of the HTO planning and simulation process, illustrating the workflow from CT scan acquisition to surgical simulation using 3D-printed jigs and tibia prototypes.
Accordingly, a minimum of six paired tibial measurements was required to achieve the desired CI precision. To account for practical variability and to strengthen the estimation, eight tibiae were included in the study. Following the institutional ethical clearance (Ref. No: IEC-INT/2025/Study-3149), high-resolution CT data of eight full-length adult tibiae were obtained from the department repository. The DICOM format scans were imported into Materialise Mimics software, a widely recognised platform for medical image processing. Within this environment, the DICOM files were segmented and converted into 3D CAD models (STL format), enabling detailed virtual planning of HTO. A board-certified orthopaedic surgeon methodically defined surgical parameters, including cutting trajectories, K-wire placements, spacer arrangements, and target values for the MPTA and PPTA. This virtual planning phase was critical, establishing the reference against which physical outcomes would be measured, simulating the preoperative process used in clinical settings.
3D printing
Patient-specific jigs were designed to fit each tibia model precisely, incorporating guides for osteotomy cuts and drill placements (Figure 2). These jigs were fabricated using an Anycubic Photon Mono X™ (Hongkong Anycubic Technology Co., Limited), a resin-based 3D printer costing approximately $550, selected for its affordability and high surface resolution. Printing was performed with PLA-based photopolymer resin (∼$40 per kg), with parameters optimised for accuracy: a layer height of 0.05 mm, an exposure time of 8 s per layer, a lifting speed of 60 mm/min, and a 2-s light-off delay. Concurrently, tibia prototypes were printed on a Creality Ender 5 Plus™ (Shenzhen Creality 3D Technology Co., Ltd.), a fused deposition modelling (FDM) printer priced around $600, using PLA filament (∼$15 per kg). The printer is one of the most economical, globally available solutions for FDM printing of models of the size of a human tibia. Printing parameters included a 0.2 mm layer height, 20 % infill density, and a 45° raster angle, balancing strength and material efficiency [19]. For post-processing, resin jigs were washed in isopropyl alcohol and cured under UV light to eliminate residual monomer. Both printers adhered to manufacturers’ calibration standards, ensuring reproducibility. This dual-printer approach allowed the creation of precise jigs and robust bone models at a fraction of the cost of industrial systems. A brief description of the associated costs, space and power specifications [20] of the two printers is presented in Table 1.

Virtual cutting (a), spacer placement (b), designed jig (c), and its attachment view (d).
Acquisition and operational requirements.
| Printer | Printer cost ($) | Space (size, mm) | Rated/peak power | Material cost per 1,000 gm | Material used/unit | Material cost per unit | Approximate cost per simulation |
|---|---|---|---|---|---|---|---|
| Anycubic photon mono X for jig | ∼550 | 270 × 290 × 475 | 120 W | ∼$40 | ∼30 g | ∼$1.2 | ∼$4.2a |
| Creality ender 5 plus for tibia | ∼600 | 632 × 666 × 619 | 550 W | ∼$15 | ∼200 g | ∼$3.0 |
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aExcludes manpower, power consumption, maintenance cost and printer depreciation.
Surgical simulation
Medial opening wedge high tibial osteotomy (MOWHTO) was simulated using the 3D-printed tibial models and corresponding patient-specific instrumentation (Figure 3). The instrumentation comprised the patient-specific cutting jigs and a spacer designed to reproduce a predetermined medial opening wedge. All procedures were performed by a board-certified orthopaedic surgeon experienced in deformity correction. The surgeon was blinded to the virtual preoperative plan and relied exclusively on the patient-specific instrumentation to achieve the intended correction.

3D printed bone and cutting jig (a), K-wire-aided attachment (b), wedge opening (c), spacer placement (d), and plate fixation (medial and anterior views) (e–f).
Each 3D-printed tibial model was secured on a stable work surface to permit controlled execution of the osteotomy. The patient-specific cutting jig was positioned on the medial aspect of the proximal tibia, conforming to predefined bony landmarks. Proper seating of the jig was confirmed visually. The jig was then fixed to the bone model using two 1.5 mm Kirschner wires inserted through pre-designed guide apertures. Prior to proceeding, the surgeon confirmed the absence of perceptible relative motion between the jig and the bone model, ensuring stable jig fixation.
An oscillating saw was introduced through the cutting slot of the jig, and the medial opening wedge osteotomy was performed along the predefined osteotomy plane. The cut was advanced in a controlled manner up to the planned lateral cortical hinge, with care taken to preserve hinge integrity. Following completion of the osteotomy, the cutting jig was removed, and a conventional high tibial osteotomy spreader was inserted into the osteotomy gap. The wedge was gradually opened under direct visual supervision, with incremental distraction performed until the patient-specific spacer could be introduced. No navigation systems or external alignment aids were utilized to evaluate the intrinsic accuracy of the jig-guided correction.
Once the desired opening was achieved using the spacer, the osteotomy was stabilised with a standard medial locking plate and screw construct applied to the proximal tibia. Fixation was intended solely to maintain the achieved alignment during subsequent measurements. There were no failed fittings and repeat prints. As the procedure was performed on a standalone model, no physiological loading, soft-tissue constraints, or biological healing considerations were incorporated. The simulation was therefore limited to assessing the geometric accuracy and reproducibility of the planned correction.
Accuracy assessment
Postoperative models were scanned using the Atos Core 300™ (GOM GmbH), a high-resolution 3D scanner, which generated detailed STL files of the realigned tibiae. This step captured the physical outcomes of the simulated surgery, allowing for a direct comparison with the virtual plan. Accordingly, the accuracy was evaluated by superimposing postoperative STL files onto preoperative virtual plans within Materialise 3Matic [21] (Figure 4(a)).

Accuracy assessment of postoperative tibial alignment using 3D model superimposition. (a) Superimposition of the postoperative tibial STL model obtained from high-resolution 3D scanning onto the preoperative virtual plan, with a color-coded surface deviation map illustrating differences between planned and achieved corrections. (b) Measurement of the medial proximal tibial angle on the overlapped models. (c) Measurement of the posterior proximal tibial angle (PPTA) on the overlapped models.
For MPTA measurement, the tibia was oriented in a true anteroposterior position, as confirmed by the symmetric appearance of the medial and lateral tibial plateaus and cortices, with the tibial shaft centrally aligned and no visual evidence of axial rotation [22] (Figure 4(b)). The tibial mechanical axis was defined by a line connecting the centre of the proximal tibial metaphysis to the centre of the distal tibial articular surface. The proximal tibial joint line was drawn as a line tangent to the tibial plateau, connecting the highest points of the medial and lateral tibial plateaus. The medial angle between these two lines was recorded as the MPTA.
For PPTA measurement, the tibia was oriented in a true lateral position with superimposition of the tibial plateaus (Figure 4(c)). The proximal tibial joint line was defined by connecting the anterior and posterior margins of the tibial plateau. The sagittal plane–modified tibial mechanical axis was constructed from a point located at approximately one-fifth of the proximal tibial joint line from the anterior cortex to the centre of the distal tibial articular surface. The posterior angle between these two lines was recorded as the PPTA [23]. The deviations in planned and achieved angles were recorded, and mean errors were calculated (Table 2). The following statistical tests were performed in MATLAB (R2024, Natick, Massachusetts: The MathWorks Inc.): (i) Paired t-tests for systematic bias, (ii) Bland-Altman plots for agreement, (iii) Linear regression (achieved ∼ planned) for proportional bias (H0: slope=1), and (iv) Two one-sided t-tests (TOST) for ±1° equivalence.
MPTA and PPTA assessment.
| Sr. no. | MPTA | PPTA | ||||||
|---|---|---|---|---|---|---|---|---|
| Planned MPTA | Achieved MPTA | Error | % Error | Planned PPTA | Achieved PPTA | Error | % Error | |
| 1 | 86.58 | 87.38 | 0.80 | 0.9 | 80.74 | 80.97 | 0.23 | 0.3 |
| 2 | 90.65 | 89.64 | −1.01 | −1.1 | 85.61 | 85.85 | 0.24 | 0.3 |
| 3 | 89.49 | 88.04 | −1.45 | −1.6 | 84.50 | 84.52 | 0.02 | 0.0 |
| 4 | 87.61 | 86.21 | −1.40 | −1.6 | 77.91 | 78.62 | 0.71 | 0.9 |
| 5 | 86.51 | 86.94 | 0.43 | 0.5 | 85.24 | 85.89 | 0.65 | 0.8 |
| 6 | 84.87 | 87.10 | 2.23 | 2.6 | 84.60 | 85.25 | 0.65 | 0.8 |
| 7 | 82.77 | 83.62 | 0.85 | 1.0 | 92.38 | 93.35 | 0.97 | 1.1 |
| 8 | 88.58 | 87.70 | −0.88 | −1.0 | 90.19 | 91.29 | 1.10 | 1.2 |
Results
MPTA outcomes
The mean difference between achieved and planned MPTA was −0.05° ± 1.32° (SD), with an absolute mean error of 1.13° (range: −1.45° to +2.23°). A paired t-test confirmed no significant systematic bias (p=0.912). Bland-Altman analysis revealed 95 % limits of agreement from −2.66° to +2.55° (Figure 5(a)), which lie within the clinically accepted ±3° tolerance for high tibial osteotomy [16], 17]. Linear regression demonstrated a strong positive correlation between planned and achieved values (R2=0.77, slope=0.59 [95 % CI: 0.264–0.921], intercept=35.452°, RMSE=0.901°) (Figure 5(b)). The slope was significantly less than 1.0 (p<0.05), indicating proportional under-correction at higher planned angles [11]. Two one-sided tests (TOST) confirmed statistical equivalence to zero within a ±1° margin (p=0.042; 90 % CI of mean difference: [−0.944°, +0.836°]), demonstrating no clinically meaningful systematic error despite the reduced slope.

Planned vs. achieved MPTA. (a) Bland-Altman plot; mean bias: −0.05°; 95 % LoA: −2.66° to +2.55° (all within ±3° tolerance). (b) Regression analysis: R2=0.77, slope=0.59 (p<0.05 vs. 1.0), RMSE=0.901°.
PPTA outcomes
Achieved-minus-planned mean difference was +0.57° ± 0.38° (SD), with an absolute mean error of 0.57° (range: +0.02° to +1.10°). The paired t-test showed a statistically significant over-correction (p=0.004). Bland-Altman analysis demonstrated tight 95 % limits of agreement from −0.17° to +1.31° (Figure 6(a)), well within clinical tolerances. Linear regression revealed an excellent correlation (R2=0.99, slope=1.04 [95 % CI: 0.970–1.112], intercept=−2.947°, RMSE=0.352°) (Figure 6(b)). The slope was not significantly different from 1.0 (p>0.05), and the intercept was not significantly different from zero, confirming proportional and additive accuracy. TOST equivalence testing confirmed statistical equivalence within a ±1° margin (p=0.007; 90 % CI of mean difference: [+0.318°, +0.824°]), assuring no clinically relevant bias despite the slight over-correction.

Planned vs. achieved PPTA. (a) Bland-Altman plot; mean bias: +0.57°; 95 % LoA: −0.17° to +1.31°. (b) Regression analysis: R2=0.99, slope=1.04 (p>0.05 vs. 1.0), RMSE=0.352°.
Discussion
Dimitrov et al. [24] demonstrated that 3D printer consistency depends on standardised material and settings. The present study confirms that low-cost printers ($550–$600) can produce clinically viable patient-specific jigs and bone models for high tibial osteotomy (HTO), directly addressing a major barrier to PSI adoption in resource-limited settings. Notably, several existing works have also validated the geometric accuracy and estimated the costs of 3D printing in the clinical domain [20], [25], [26], [27]. However, to the best of our knowledge, we could not find a study that evaluates the accuracy and efficacy of surgical simulations performed on 3D printers priced below $1,000, and with a per-simulation consumable cost of less than $5. For instance, in a medical 3D printing cost estimation study by Clifton et al. [25] a ∼$5,000 FDM printer was used, which works on proprietary $100/kg PLA material. On the contrary, the $600 printer used in the present study could work on a $15/kg general-purpose PLA. Similarly, unlike prior isolated evaluations, our study validates the full PSI workflow–from CT planning to simulated osteotomy and postoperative alignment–using sub-$1,000 printers and less than $5 simulation cost. The ultimate goal, nevertheless, is to take 3D printed PSI into operating rooms of resource-limited settings.
For MPTA, TOST confirmed equivalence to zero within ±1°, although linear regression indicated proportional under-correction at higher planned angles. The overall findings suggest adequate accuracy but limited precision, possibly due to saw blade divergence. The correlation of PPTA measurements was slightly superior to MPTA, with near-perfect regression parameters and ±1° equivalence in TOST. The superior PPTA correlation likely reflects better jig constraint in the sagittal plane. The moderate MPTA vs. excellent PPTA correlation also highlights a design opportunity, i.e., enhancing medial jig stability (e.g., anti-rotation tabs) could improve frontal-plane reliability. This differential outcome also underscores the value of multi-plane validation in PSI development.
Affordable 3D printing enables tangible preoperative rehearsal, allowing surgeons to anticipate wedge gaps and refine cuts [28]. Prototypes also serve as ethical, repeatable training tools [29] and patient education aids, improving consent and trust. By reducing PSI cost from thousands to hundreds of dollars, this approach democratises access, especially in resource-limited settings.
Despite these strengths, the preclinical design with only eight prototypes limits generalisability. Additionally, the use of 3D printed PLA-based materials in clinical settings is less explored, although their use as PSI for human studies has commenced [30], and they are considered high-potential candidates for biodegradable implants and scaffolds [31], 32]. Common PLA is cost-effective; however, lacks the durability to withstand intraoperative forces and heat. The advanced heat-resistant variants, such as the one used in Menozzi et al. [30], successfully developed 80 personalised devices for 47 surgeries. Nevertheless, the cost of supported 3D printers and consumables shall go up when using such variants. Therefore, exploring the use of such variants in sub-$1,000 printers should be the next step in the direction of affordable PSI. Alternatively, low-temperature sterilisation methods can be explored to preserve jig integrity in economic PLA variants – options include ethylene oxide (EtO) at 37–55 °C and vaporised hydrogen peroxide (VHP) plasma technology [33], [34], [35]. Material upgrades to autoclavable, biocompatible resins (e.g., Siraya Tech Blu, Formlabs Surgical Guide, or PEEK composites) can also be tested for mechanical strength and thermal stability. A complete cost-effectiveness analysis comparing total workflow cost (printer, resin, sterilisation) against conventional instrumentation is also warranted. Finally, the methodology needs to be validated for in vivo studies, and a range of surgical procedures in line with Menozzi et al. [30].
Conclusions
The present proof-of-concept demonstrates that a low-cost 3D-printed PSI developed with ∼$500 printer and material cost of ∼$2 can achieve clinically observed accuracy in a preclinical simulation setting. Incorporation of high-temperature, biocompatible variants or alternate sterilisation methods may increase the clinical usage costs. Nevertheless, the concept of using two low-cost 3D printers with different capabilities (one suited for large bone models and the other suited for accurate surface recreation) seems suitable for pre-planning, training and education at this stage and may be extended to clinical settings with advancements in 3D printers and improved material characteristics.
Funding source: Indian Council of Medical Research
Award Identifier / Grant number: (IIRP-2023–783)
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Research ethics: Approval received from the Institute Ethics Committee (Ref. No: IEC-INT/2025/Study-3149).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Grant No: IIRP-2023–783, ICMR, Department of Health and Family Welfare, Govt. of India.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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