C-Arm Positioning for Spinal Standard Projections in Different Intra-operative Settings

Kausch, Lisa; Thomas, Sarina; Kunze, Holger; Norajitra, Tobias; Klein, André; Barbari, Jan Siad El; Privalov, Maxim; Vetter, Sven; Mahnken, Andreas H.; Maier-Hein, Lena; Maier‐Hein, Klaus H.

doi:10.1007/978-3-030-87202-1_34

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…Furthermore, previous studies have shown that planning methods are highly susceptible to rotational deviations between the underlying anatomy and the standard projection [87,88]. We believe that extending our method with an automatic assessment [89,90] of this mismatch could assist in preventing target malpositioning and improve the planning's reliability and clinical acceptance. Another desirable extension is to combine our method with surgical tool recognition.…”

Section: Discussionmentioning

confidence: 89%

Multi-Stage Platform for (Semi-)Automatic Planning in Reconstructive Orthopedic Surgery

et al. 2022

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Intricate lesions of the musculoskeletal system require reconstructive orthopedic surgery to restore the correct biomechanics. Careful pre-operative planning of the surgical steps on 2D image data is an essential tool to increase the precision and safety of these operations. However, the plan’s effectiveness in the intra-operative workflow is challenged by unpredictable patient and device positioning and complex registration protocols. Here, we develop and analyze a multi-stage algorithm that combines deep learning-based anatomical feature detection and geometric post-processing to enable accurate pre- and intra-operative surgery planning on 2D X-ray images. The algorithm allows granular control over each element of the planning geometry, enabling real-time adjustments directly in the operating room (OR). In the method evaluation of three ligament reconstruction tasks effect on the knee joint, we found high spatial precision in drilling point localization (ε<2.9mm) and low angulation errors for k-wire instrumentation (ε<0.75∘) on 38 diagnostic radiographs. Comparable precision was demonstrated in 15 complex intra-operative trauma cases suffering from strong implant overlap and multi-anatomy exposure. Furthermore, we found that the diverse feature detection tasks can be efficiently solved with a multi-task network topology, improving precision over the single-task case. Our platform will help overcome the limitations of current clinical practice and foster surgical plan generation and adjustment directly in the OR, ultimately motivating the development of novel 2D planning guidelines.

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Section: Discussionmentioning

confidence: 89%

Multi-Stage Platform for (Semi-)Automatic Planning in Reconstructive Orthopedic Surgery

et al. 2022

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“…Further studies also showed that planning methods are highly susceptible to rotational aberrations between the underlying anatomy and the radiological standard projection 29,30 . We believe that extending our automated MPFL planning method to include automatic detection of the correct lateral standard projection of the knee joint could help prevent radiological misprojection of the target and improve planning reliability and clinical acceptability 31,32 …”

Section: Discussionmentioning

confidence: 92%

Software‐based method for automated intraoperative planning of Schoettle Point in surgical medial patellofemoral ligament reconstruction: A comparative validation study

Privalov,

Kordon,

Kunze

et al. 2024

Robotics Computer Surgery

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BackgroundThe aim of the study was to validate a software‐based planning method for the Schoettle Point and to evaluate precision and time efficiency of its live overlay on the intraoperative X‐ray.MethodsA software‐based method was compared with surgeons' manual planning in an inter‐ and intrarater study. Subsequently, K‐wire placement was performed with and without an overlay of the planning. The time used and the precision achieved were statistically compared.ResultsThe average deviation between the surgeons (1.68 mm; 2.26 mm) was greater than the discrepancy between the surgeons and the software‐based planning (1.30 mm; 1.38 mm). In the intrarater comparison, software‐based planning provided consistent results. Live overlay showed a significantly lower positioning error (0.9 ± 0.5 mm) compared with that without overlay (3.0 ± 1.4 mm, p = 0.000; 3.1 ± 1.4 mm, p = 0.001). Live overlay did not achieve a significant time gain (p = 0.393; p = 0.678).ConclusionThe software‐based planning and live overlay of the Schoettle Point improves surgical precision without negatively affecting time efficiency.

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“…In diagnostic medical image analysis, GAN-based synthesis of novel samples has been used to augment available training data for magnetic resonance imaging [43][44][45][46][47][48] , CT 46,49 , ultrasound 50 , retinal [51][52][53] , skin lesion 54,55 and CXR 56 images. In computer-assisted interventions, early successes on the Sim2Real problem include analysis on endoscopic images 3,57-59 and intra-operative X-ray [60][61][62] . The controlled study here validates this approach in the X-ray domain by showing that Sim2Real compares favourably to Real2Real training.…”

Section: Discussionmentioning

confidence: 99%

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis

Gao

Killeen

et al. 2023

Nat Mach Intell

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Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.Advances in robotics and artificial intelligence (AI) are bringing autonomous surgical systems closer to reality. However, developing the AI backbones of such systems currently depends on collecting training data during routine surgeries. This remains one of the largest barriers to widespread use of AI systems in interventional clinical settings, versus triage or diagnostic settings, as the acquisition and annotation of interventional data is time intensive and costly. Furthermore, while this approach can contribute to the automation or streamlining of existing surgical workflows, robotic and autonomous systems promise even more substantial advances: novel and super-human techniques that improve outcomes, save time or mitigate human error. This is perhaps the most exciting frontier of computer-assisted intervention research.Conventional approaches for curating data for AI development (that is, sourcing it retroactively from clinical practice) are insufficient for training AI models that benefit interventions that use novel instrumentation, different access points or more flexible imaging. This is because they are, by definition, incompatible with contemporary clinical practices and such data do not emerge from routine care. Furthermore, these novel systems are not readily approved, and thus not easily or quickly introduced into clinical practice. Ex vivo experimentation

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C-Arm Positioning for Spinal Standard Projections in Different Intra-operative Settings

Cited by 11 publications

References 23 publications

Multi-Stage Platform for (Semi-)Automatic Planning in Reconstructive Orthopedic Surgery

Multi-Stage Platform for (Semi-)Automatic Planning in Reconstructive Orthopedic Surgery

Software‐based method for automated intraoperative planning of Schoettle Point in surgical medial patellofemoral ligament reconstruction: A comparative validation study

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis

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