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Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Orthopedics".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 16010

Special Issue Editor

Dr. Kouroush Kabir

E-Mail Website
Guest Editor
Department of Trauma Surgery, Orthopaedics & Sports Medicine, Helios University Hospital Wuppertal, 42283 Wuppertal, Germany
Interests: minimally invasive and endoscopic pelvic surgery; computer-navigated and robotic surgery of the spine and pelvis; telemedicine; AI in orthopaedic and trauma surgery; sustainability in surgery; patient-centric healthcare with a particular focus on orthogeriatric and sports medicine

Special Issue Information

Dear Colleagues,

This Special Issue, "Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques", will explore the groundbreaking integration of AI and robotic technologies in orthopedic surgery. It will delve into the latest developments, research findings, and clinical applications of AI-driven algorithms and robotic-assisted surgical procedures, showcasing their potential to enhance precision, improve patient outcomes, and reduce recovery times. Contributions will cover a range of topics, including the role of AI in preoperative planning, intraoperative navigation, and postoperative care, as well as the recent advancements in minimally invasive robotic surgery techniques. This Special Issue seeks to foster discussions on the challenges, ethical considerations, and future directions of AI and robotics in orthopedics, presenting insights from researchers and clinicians. By bridging the gap between technology and clinical practice, this Special Issue will pave the way for innovative solutions and transformative approaches in the surgical management of orthopedic conditions.

Dr. Kouroush Kabir
Guest Editor

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Keywords

  • orthopedic surgery
  • Artificial Intelligence (AI)
  • robotic surgery techniques
  • preoperative planning
  • intraoperative navigation
  • postoperative care
  • minimally invasive surgery
  • surgical precision
  • patient-centric outcomes and patient-specific implants
  • ethical considerations in AI and robotics

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Published Papers (8 papers)

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Research

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8 pages, 267 KiB  
Article
Inherent Bias in ROSA® Zimmer Biomet Pre-Op Planning Using 2D to 3D X-Atlas® Coronal Knee Axis Measurement
by Michał A. Duchniewicz, Aly Shaaban, Manuel Müller, Philip M. Anderson, Lars Goebel, Patrick Orth, Milan A. Wolf, Felix Bachelier, Stefan Landgraeber and Philipp Winter
J. Clin. Med. 2025, 14(5), 1698; https://doi.org/10.3390/jcm14051698 - 3 Mar 2025
Viewed by 880
Abstract
Background: Robotic assistance is considered capable of improving precision and outcomes of total knee replacement. We assessed the inherent biases, pre-procedural planning accuracy using 2D to 3D X-Atlas®, and final knee axis outcomes of the ROSA® Knee System (Zimmer Biomet, [...] Read more.
Background: Robotic assistance is considered capable of improving precision and outcomes of total knee replacement. We assessed the inherent biases, pre-procedural planning accuracy using 2D to 3D X-Atlas®, and final knee axis outcomes of the ROSA® Knee System (Zimmer Biomet, Warsaw, IN, USA). Methods: A total of 55 patients who underwent robotic-assisted knee replacement using ROSA® Knee System (Zimmer Biomet, Warsaw, IN, USA) at a single center were included. Pre-procedural measurements performed by ROSA were compared to those performed by senior consultants. Component sizes predicted by ROSA® were compared to those implanted. A final axis measurement was taken during the procedure. Results: Femur components were exactly matched in (83.64%) cases, accurately matched in a further 8 (14.55%), and inaccurately matched for only 1 (1.82%). Tibial component sizes were exactly matched by the planning for 39 (70.91%), accurately for 12 (21.82%), and inaccurately for 4 (7.27%). ANOVA did not show statistically significant differences between the predicted and implanted femur (p = 0.96) nor the tibia components (p = 0.27). We show that ROSA® pre-procedural planning has a statistically significant bias (p = 0.001), with a deviation of 0.83 degrees into varus, when assessing the knee axis in the coronal plane, compared to senior consultant measurements. The average of the final coronal knee axis was 0.37 degrees in varus (SD = 2.49). Conclusions: ROSA® accurately predicts implanted component sizes. Despite the small and statistically significant varus bias in initial knee axis assessment, the system results lay within the ±3° of neutral knee axis, which is the widely accepted knee replacement standard. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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12 pages, 2235 KiB  
Article
Functional Alignment Achieved a More Balanced Knee After Robotic Arm-Assisted Total Knee Arthroplasty than Modified Kinematic Alignment
by Hong-Yeol Yang, Jong-Keun Seon, Ji-Hyeon Yim, Dong-Hyun Lee and Eun-Kyoo Song
J. Clin. Med. 2025, 14(3), 820; https://doi.org/10.3390/jcm14030820 - 26 Jan 2025
Cited by 3 | Viewed by 1209
Abstract
Background: The aim of this study was to evaluate the balance in extension and flexion achievable after total knee arthroplasty (TKA) using a modified kinematic alignment (KA) plan and the subsequent balance achievable after adjusting the component based on the functional alignment [...] Read more.
Background: The aim of this study was to evaluate the balance in extension and flexion achievable after total knee arthroplasty (TKA) using a modified kinematic alignment (KA) plan and the subsequent balance achievable after adjusting the component based on the functional alignment (FA) principle. Methods: This retrospective cohort study included 100 consecutive patients who underwent primary TKA for knee osteoarthritis through an image-based robotic system in a single center between October 2021 and February 2022. Whether modified KA or FA could achieve a balanced knee was evaluated by assessing the ligament balance in the medial and lateral compartments using a robotic system at extension and 90° flexion. Balance was defined as a difference of ≤2 mm between the compartments. Component positioning was adjusted within limits based on the functional positioning principles to achieve balance. Implant positioning and balance in extension and 90° flexion were compared between the modified KA plan (n = 100) and after FA adjustments (n = 100). Results: FA achieved significantly better balance in extension (FA, 99.0% vs. modified KA, 86.0%; p = 0.001) and flexion (98.0% vs. 43.0%; p < 0.001) than the modified KA plan. The mean difference in gap balance in extension (FA, 0.1 mm vs. modified KA, 0.6 mm; p = 0.001) and flexion (0.1 mm vs. 2.3 mm; p < 0.001) was also significant between the two techniques. The femoral component was positioned more externally rotated relative to the transepicondylar axis (FA, 2.5° vs. modified KA, 0.0°; p < 0.001) to obtain balanced targets. There were significant improvements in the patient-reported outcome measures between preoperative and postoperative assessments two years after TKA (all p < 0.05). Conclusions: FA consistently achieved superior balance in both extension and flexion following TKA compared with modified KA without altering the soft tissue envelope, leading to significant improvements in clinical outcomes at the two-year follow-up. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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16 pages, 3179 KiB  
Article
ChatGPT’s Performance in Spinal Metastasis Cases—Can We Discuss Our Complex Cases with ChatGPT?
by Stephan Heisinger, Stephan N. Salzmann, Wolfgang Senker, Stefan Aspalter, Johannes Oberndorfer, Michael P. Matzner, Martin N. Stienen, Stefan Motov, Dominikus Huber and Josef Georg Grohs
J. Clin. Med. 2024, 13(24), 7864; https://doi.org/10.3390/jcm13247864 - 23 Dec 2024
Cited by 1 | Viewed by 1497
Abstract
Background: The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT-4, is transforming healthcare. ChatGPT’s potential to assist in decision-making for complex cases, such as spinal metastasis treatment, is promising but widely untested. Especially in cancer patients who develop spinal [...] Read more.
Background: The integration of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT-4, is transforming healthcare. ChatGPT’s potential to assist in decision-making for complex cases, such as spinal metastasis treatment, is promising but widely untested. Especially in cancer patients who develop spinal metastases, precise and personalized treatment is essential. This study examines ChatGPT-4’s performance in treatment planning for spinal metastasis cases compared to experienced spine surgeons. Materials and Methods: Five spine metastasis cases were randomly selected from recent literature. Consequently, five spine surgeons and ChatGPT-4 were tasked with providing treatment recommendations for each case in a standardized manner. Responses were analyzed for frequency distribution, agreement, and subjective rater opinions. Results: ChatGPT’s treatment recommendations aligned with the majority of human raters in 73% of treatment choices, with moderate to substantial agreement on systemic therapy, pain management, and supportive care. However, ChatGPT’s recommendations tended towards generalized statements, with raters noting its generalized answers. Agreement among raters improved in sensitivity analyses excluding ChatGPT, particularly for controversial areas like surgical intervention and palliative care. Conclusions: ChatGPT shows potential in aligning with experienced surgeons on certain treatment aspects of spinal metastasis. However, its generalized approach highlights limitations, suggesting that training with specific clinical guidelines could potentially enhance its utility in complex case management. Further studies are necessary to refine AI applications in personalized healthcare decision-making. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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11 pages, 770 KiB  
Article
Clinical Trends and Outcomes in Technology-Assisted Total Hip Arthroplasty
by Konstantinos Oikonomou, Nicholas R. Kiritsis, Haleigh M. Hopper, James R. Satalich, Conor N. O’Neill, Brady Ernst and Jibanananda Satpathy
J. Clin. Med. 2024, 13(20), 6035; https://doi.org/10.3390/jcm13206035 - 10 Oct 2024
Viewed by 1158
Abstract
Background/Objectives: In recent years, there has been a widespread focus on implementing technology in total hip arthroplasty (THA) to further improve precision and outcomes. This study aimed to identify recent trends in the utilization, clinical variables, and rate of adverse events for technology-assisted [...] Read more.
Background/Objectives: In recent years, there has been a widespread focus on implementing technology in total hip arthroplasty (THA) to further improve precision and outcomes. This study aimed to identify recent trends in the utilization, clinical variables, and rate of adverse events for technology-assisted THA (TA-THA) and compare the outcomes to those of conventional THA. Methods: This retrospective cohort analysis of the ACS-NSQIP database queried data on THA patients (CPT 27130) from 2015 to 2020. Technology assistance was identified with CPT 20985, 0054T, and 0055T. Matched cohorts were created to compare clinical comorbidities and adverse events. Results: This analysis included 219,216 conventional THAs and 2258 cases utilizing TA-THA. The number and percentage of surgeries utilizing technology, as well as the average operative time, consistently rose from 2015 to 2019, with all declining in 2020. Length of stay decreased yearly from 2015 to 2019, with an increase in 2020. There were no significant differences in the incidence of adverse events by year. Matched cohort analysis demonstrated that TA-THA led to longer operative times (102.6 ± 35.6 vs. 91.6 ± 37.4 min, p < 0.001) and a shorter average length of stay (1.6 ± 1.4 vs. 2.0 ± 1.9 days, p < 0.001). Transfusion rates were higher in the TA-THA cohort (6.0% vs. 4.4%, p = 0.013). Conclusions: The usage of TA-THA increased from 2015 to 2019, with declines during 2020. TA-THA led to longer operative times, increased transfusion rates, and no difference in the incidence of adverse events compared to conventional arthroplasty. These findings demonstrate that TA-THA is growing in popularity without a significant improvement in short-term complication rates. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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11 pages, 2810 KiB  
Article
A Novel Model Based on CNN–ViT Fusion and Ensemble Learning for the Automatic Detection of Pes Planus
by Kamil Doğan, Turab Selçuk and Abdurrahman Yılmaz
J. Clin. Med. 2024, 13(16), 4800; https://doi.org/10.3390/jcm13164800 - 15 Aug 2024
Cited by 2 | Viewed by 2071
Abstract
Background: Pes planus, commonly known as flatfoot, is a condition in which the medial arch of the foot is abnormally low or absent, leading to the inner part of the foot having less curvature than normal. Symptom recognition and errors in diagnosis [...] Read more.
Background: Pes planus, commonly known as flatfoot, is a condition in which the medial arch of the foot is abnormally low or absent, leading to the inner part of the foot having less curvature than normal. Symptom recognition and errors in diagnosis are problems encountered in daily practice. Therefore, it is important to improve how a diagnosis is made. With the availability of large datasets, deep neural networks have shown promising capabilities in recognizing foot structures and accurately identifying pes planus. Methods: In this study, we developed a novel fusion model by combining the Vgg16 convolutional neural network (CNN) model with the vision transformer ViT-B/16 to enhance the detection of pes planus. This fusion model leverages the strengths of both the CNN and ViT architectures, resulting in improved performance compared to that in reports in the literature. Additionally, ensemble learning techniques were employed to ensure the robustness of the model. Results: Through a 10-fold cross-validation, the model demonstrated high sensitivity, specificity, and F1 score values of 97.4%, 96.4%, and 96.8%, respectively. These results highlight the effectiveness of the proposed model in quickly and accurately diagnosing pes planus, making it suitable for deployment in clinics or healthcare centers. Conclusions: By facilitating early diagnosis, the model can contribute to the better management of treatment processes, ultimately leading to an improved quality of life for patients. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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11 pages, 1460 KiB  
Article
Validation of a Three-Dimensional Weight-Bearing Measurement Protocol for Medial Open-Wedge High Tibial Osteotomy
by Sandro Hodel, Julian Hasler, Tabitha Arn Roth, Andreas Flury, Cyrill Sutter, Sandro F. Fucentese, Philipp Fürnstahl and Lazaros Vlachopoulos
J. Clin. Med. 2024, 13(5), 1280; https://doi.org/10.3390/jcm13051280 - 23 Feb 2024
Cited by 4 | Viewed by 1405
Abstract
Three-dimensional (3D) deformity assessment and leg realignment planning is emerging. The aim of this study was to (1) validate a novel 3D planning modality that incorporates the weight-bearing (WB) state (3D WB) by comparing it to existing modalities (3D non-weight-bearing (NWB), 2D WB) [...] Read more.
Three-dimensional (3D) deformity assessment and leg realignment planning is emerging. The aim of this study was to (1) validate a novel 3D planning modality that incorporates the weight-bearing (WB) state (3D WB) by comparing it to existing modalities (3D non-weight-bearing (NWB), 2D WB) and (2) evaluate the influence of the modality (2D vs. 3D) and the WB condition on the measurements. Three different planning and deformity measurement protocols were analyzed in 19 legs that underwent medial open-wedge high tibial osteotomy (HTO): (1) a 3D WB protocol, after 2D/3D registration of 3D CT models onto the long-leg radiograph (LLR) (3D WB), (2) a 3D NWB protocol based on the 3D surface models obtained in the supine position (3D NWB), and (3) a 2D WB protocol based on the LLR (2D WB). The hip–knee–ankle angle (HKA), joint line convergence angle (JLCA), and the achieved surgical correction were measured for each modality and patient. All the measurement protocols demonstrated excellent intermodal agreement for the achieved surgical correction, with an ICC of 0.90 (95% CI: 0.76–0.96)) (p < 0.001). Surgical correction had a higher mean absolute difference compared to the 3D opening angle (OA) when measured with the WB protocols (3D WB: 2.7 ± 1.8°, 3D NWB: 1.9 ± 1.3°, 2D WB: 2.2 ± 1.3°), but it did not show statistical significance. The novel planning modality (3D WB) demonstrated excellent agreement when measuring the surgical correction after HTO compared to existing modalities. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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Review

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13 pages, 3388 KiB  
Review
Artificial Intelligence in Scoliosis: Current Applications and Future Directions
by Haozhi Zhang, Changfa Huang, Daoyun Wang, Kuan Li, Xiao Han, Xin Chen and Zheng Li
J. Clin. Med. 2023, 12(23), 7382; https://doi.org/10.3390/jcm12237382 - 29 Nov 2023
Cited by 12 | Viewed by 5127
Abstract
Scoliosis is a three-dimensional deformity of lateral bending and rotation of the spine. Artificial intelligence (AI) is a set of theories and techniques for studying artificial intelligence, which realizes machine intelligence by simulating and expanding human intelligence. With the continuous development of the [...] Read more.
Scoliosis is a three-dimensional deformity of lateral bending and rotation of the spine. Artificial intelligence (AI) is a set of theories and techniques for studying artificial intelligence, which realizes machine intelligence by simulating and expanding human intelligence. With the continuous development of the multidisciplinary integration trend of modern medicine, artificial intelligence is used more and more in the diagnosis and treatment of scoliosis. Artificial intelligence has been widely used in the study of scoliosis and has penetrated into all fields of clinical practice of scoliosis. At present, artificial intelligence has shown good application prospects in early screening, diagnosis, treatment decision making, intraoperative operation, and prognosis prediction of scoliosis. This paper mainly summarizes the application of artificial intelligence in the clinical practice of scoliosis, and briefly introduces the AI model and its specific applications. In addition, this paper also discusses the limitations and future development of artificial intelligence. In the future, artificial intelligence will bring greater convenience to the diagnosis and treatment of scoliosis and provide better therapeutic effects for patients. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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Other

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25 pages, 9801 KiB  
Systematic Review
Comparative Efficacy and Safety of Robot-Assisted vs. Freehand Screw Placement in Femoral Neck Fractures: An Updated Systematic Review and Meta-Analysis
by Ümit Mert, Mohamad Agha Mahmoud, Maher Ghandour, Ahmad Al Zuabi, Marco Speicher, Philipp Kobbe, Klemens Horst, Frank Hildebrand and Koroush Kabir
J. Clin. Med. 2024, 13(17), 5072; https://doi.org/10.3390/jcm13175072 - 27 Aug 2024
Cited by 1 | Viewed by 1544
Abstract
Background: Femoral neck fractures pose significant surgical challenges with high morbidity and mortality. Traditional freehand screw placement often yields variable outcomes. Recent robotic advancements offer a promising alternative with enhanced precision. Methods: This systematic review compares the efficacy and safety of robot-assisted versus [...] Read more.
Background: Femoral neck fractures pose significant surgical challenges with high morbidity and mortality. Traditional freehand screw placement often yields variable outcomes. Recent robotic advancements offer a promising alternative with enhanced precision. Methods: This systematic review compares the efficacy and safety of robot-assisted versus freehand techniques. A comprehensive literature search across multiple databases up to July 2024 included studies comparing both techniques. Primary outcomes were the union rate and time, functional outcomes, operative time, intraoperative parameters, and complication rates. Meta-regression analyses identified treatment response determinants. Results: Twenty-four studies (1437 patients) were included. Robot-assisted screw placement significantly improved the union rate, reduced the union time, and showed superior functional outcomes. Additionally, it resulted in shorter operative times, less intraoperative blood loss, and fewer instances of fluoroscopy and guide pin insertion. The risk of femoral neck necrosis was notably lower with robotic assistance. Meta-regression highlighted the robot type, patient age, and sample size as significant factors. Conclusions: Despite the promise of robot-assisted screw placement, limitations exist. The evidence being mainly from China raises concerns about generalizability. The lack of long-term follow-up data hinders assessment of technique durability. Unreported surgeon expertise levels and learning curves affect result validity. High initial costs and steep learning curves of robotic systems also present barriers to widespread adoption. Full article
(This article belongs to the Special Issue Advances in Surgical Management of Orthopedics—Artificial Intelligence (AI) Applications and Robotic Surgery Techniques)
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