A Biomechanical Study on the Use of Curved Drilling Technique for Treatment of Osteonecrosis of Femoral Head

Bakhtiarinejad, Mahsan; Alambeigi, Farshid; Chamani, Alireza; Unberath, Mathias; Khanuja, Harpal S.; Armand, Mehran

doi:10.1007/978-3-030-15923-8_7

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…We consider a continuum manipulator (CM) as the target object. CMs have been investigated in minimally invasive robot-assisted orthopaedic procedures because of their substantial dexterity and stiffness 17,18 , but they are not currently used clinically nor easily manufactured for extensive cadaveric testing. Using SyntheX, we address CM detection, which consists of segmenting the CM body and predicting distinct landmarks in the X-ray images.…”

Section: Surgical Robotic Tool Detectionmentioning

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

Self Cite

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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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Section: Surgical Robotic Tool Detectionmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…We consider a continuum manipulator as the target object. Continuum manipulators (CMs) have been investigated in minimally-invasive robot-assisted orthopedic procedures because of their significant dexterity and stiffness 19,20 , but they are not currently used clinically nor easily manufactured for extensive cadaveric testing. Using SyntheX, we address CM detection, which consists of segmenting the CM body and predicting distinct landmarks in the X-ray images.…”

Section: Surgical Robotic Tool Detectionmentioning

confidence: 99%

SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments

Cong¹,

Killeen²,

Yi-cheng³

et al. 2022

Preprint

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Artificial intelligence (AI) now enables automated interpretation of medical images for clinical use. However, AI's potential use for interventional images (versus those involved in triage or diagnosis), such as for guidance during surgery, remains largely untapped. This is because surgical AI systems are currently trained using post hoc analysis of data collected during live surgeries, which 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 or adaptation techniques, results in models that on real data perform comparably to models trained on a precisely matched real data training set. Because synthetic generation of training data from human-based models scales easily, 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. We demonstrate the potential of SyntheX on three clinical tasks: Hip image analysis, surgical robotic tool detection, and COVID-19 lung lesion segmentation. SyntheX provides an opportunity to drastically accelerate the conception, design, and evaluation of intelligent systems for X-ray-based medicine. In addition, simulated image environments provide the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time, or mitigate human error, freed from the ethical and practical considerations of live human data collection.

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Towards an Autonomous Minimally Invasive Spinal Fixation Surgery Using a Concentric Tube Steerable Drilling Robot

Sharma,

Go,

Bonyun

et al. 2024

2024 International Symposium on Medical Robotics (ISMR)

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A Biomechanical Study on the Use of Curved Drilling Technique for Treatment of Osteonecrosis of Femoral Head

Cited by 7 publications

References 29 publications

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

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

SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments

Towards an Autonomous Minimally Invasive Spinal Fixation Surgery Using a Concentric Tube Steerable Drilling Robot

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