Künstliche Intelligenz in der orthopädisch-unfallchirurgischen Radiologie

Rohde, Stefan; Münnich, Nico

doi:10.1007/s00132-022-04293-y

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Thus, AI is used for monitoring, predicting events, guidance, management, and surgical room logistics 1 . A special application is the EOS™ (ATEC Spine Group, Paris, France), which allows 3D simulation through the analysis and recognition of patterns from computed tomography (CT) datasets of the axial skeleton and semi-automatic calculations of length and angle based on a 2-DX digital X-ray image 9 . This not only reduces motion anxiety but also offers the opportunity to experiment with different approaches and tactics, increasing confidence and improving decision-making.…”

Section: Resultsmentioning

confidence: 99%

Use of artificial intelligence in the surgical field: advantages and implications

Menezes,

Krubniki,

Fonseca

et al. 2024

Int. J. Res. Publ. Rev.

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It is evident that we are on the brink of a future filled with revolutionary possibilities ranging from risk reduction to improved decision-making.However, artificial intelligence (AI) is a novel tool and raises ethical concerns in its application. Objectives:To analyze the current use of AI in the surgical field and its implications.Methods: This is a narrative review conducted on the National Library of Medicine (NLM) databases from the past 5 years. This process consisted of four stages: identification, selection, eligibility, and inclusion.Results: Algorithms can provide estimates of success probabilities, potential complications, and postoperative recovery. This approach allows surgeons to guide patients more precisely and engage in shared decision-making. Conclusion:This tool does not replace surgeons' decision-making but serves as a valuable aid in the pursuit of the best outcome. The success of this approach depends on the harmony between human knowledge and machine processing capabilities.

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Section: Resultsmentioning

confidence: 99%

Use of artificial intelligence in the surgical field: advantages and implications

Menezes,

Krubniki,

Fonseca

et al. 2024

Int. J. Res. Publ. Rev.

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“…Artificial intelligence (AI) may improve diagnostic accuracy and treatment plans based on large amounts of patient data to determine which painful or tumorous conditions may benefit from intervention and which not—a break with traditional laboratory or imaged-based medical necessity criteria for surgery [ 26 , 83 , 84 , 85 ]. AI could also assist surgeons during surgery, providing real-time feedback and guidance [ 86 , 87 , 88 ]. Three-dimensional printing will further facilitate and simplify manufacturing-customized implants and prosthetics [ 89 , 90 , 91 ].…”

Section: Impact Of Transformative Technologies and Targeted Care Modelsmentioning

confidence: 99%

The Changing Environment in Postgraduate Education in Orthopedic Surgery and Neurosurgery and Its Impact on Technology-Driven Targeted Interventional and Surgical Pain Management: Perspectives from Europe, Latin America, Asia, and The United States

Lewandrowski

Elfar

et al. 2023

JPM

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Personalized care models are dominating modern medicine. These models are rooted in teaching future physicians the skill set to keep up with innovation. In orthopedic surgery and neurosurgery, education is increasingly influenced by augmented reality, simulation, navigation, robotics, and in some cases, artificial intelligence. The postpandemic learning environment has also changed, emphasizing online learning and skill- and competency-based teaching models incorporating clinical and bench-top research. Attempts to improve work–life balance and minimize physician burnout have led to work-hour restrictions in postgraduate training programs. These restrictions have made it particularly challenging for orthopedic and neurosurgery residents to acquire the knowledge and skill set to meet the requirements for certification. The fast-paced flow of information and the rapid implementation of innovation require higher efficiencies in the modern postgraduate training environment. However, what is taught typically lags several years behind. Examples include minimally invasive tissue-sparing techniques through tubular small-bladed retractor systems, robotic and navigation, endoscopic, patient-specific implants made possible by advances in imaging technology and 3D printing, and regenerative strategies. Currently, the traditional roles of mentee and mentor are being redefined. The future orthopedic surgeons and neurosurgeons involved in personalized surgical pain management will need to be versed in several disciplines ranging from bioengineering, basic research, computer, social and health sciences, clinical study, trial design, public health policy development, and economic accountability. Solutions to the fast-paced innovation cycle in orthopedic surgery and neurosurgery include adaptive learning skills to seize opportunities for innovation with execution and implementation by facilitating translational research and clinical program development across traditional boundaries between clinical and nonclinical specialties. Preparing the future generation of surgeons to have the aptitude to keep up with the rapid technological advances is challenging for postgraduate residency programs and accreditation agencies. However, implementing clinical protocol change when the entrepreneur–investigator surgeon substantiates it with high-grade clinical evidence is at the heart of personalized surgical pain management.

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“…Visual recognition with convolutional neural network (CNN) deep learning algorithms is becoming increasingly popular in many fields, including orthopedics and traumatology. [6][7][8] Given the challenges of identifying implants among a substantial number of potential manufacturer models, a CNN deep learning algorithm could serve as a promising method to facilitate the instantaneous identification of knee arthroplasty implants, considering the complexities associated with their characterization.…”

Section: Introductionmentioning

confidence: 99%

Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

GÖLGELİOĞLU,

AŞKIN,

GÜNDOĞDU

et al. 2023

Anatolian Current Medical Journal

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Aims: This study aimed to investigate the use of a convolutional neural network (CNN) deep learning approach to accurately identify total knee arthroplasty (TKA) implants from X-ray radiographs. Methods: This retrospective study employed a deep learning CNN system to analyze pre-revision and post-operative knee X-rays from TKA patients. We excluded cases involving unicondylar and revision knee replacements, as well as low-quality or unavailable X-ray images and those with other implants. Ten cruciate-retaining TKA replacement models were assessed from various manufacturers. The training set comprised 69% of the data, with the remaining 31% in the test set, augmented due to limited images. Evaluation metrics included accuracy and F1 score, and we developed the software in Python using the TensorFlow library for the CNN method. A computer scientist with AI expertise managed data processing and testing, calculating specificity, sensitivity, and accuracy to assess CNN performance. Results: In this study, a total of 282 AP and lateral X-rays from 141 patients were examined, encompassing 10 distinct knee prosthesis models from various manufacturers, each with varying X-ray counts. The CNN technique exhibited flawless accuracy, achieving a 100% identification rate for both the manufacturer and model of TKA across all 10 different models. Furthermore, the CNN method demonstrated exceptional specificity and sensitivity, consistently reaching 100% for each individual implant model. Conclusion: This study underscores the impressive capacity of deep learning AI algorithms to precisely identify knee arthroplasty implants from X-ray radiographs. It highlights AI’s ability to detect subtle changes imperceptible to humans, execute precise computations, and handle extensive data. The accurate recognition of knee replacement implants using AI algorithms prior to revision surgeries promises to enhance procedure efficiency and outcomes.

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Künstliche Intelligenz in der orthopädisch-unfallchirurgischen Radiologie

Cited by 6 publications

References 25 publications

Use of artificial intelligence in the surgical field: advantages and implications

Use of artificial intelligence in the surgical field: advantages and implications

The Changing Environment in Postgraduate Education in Orthopedic Surgery and Neurosurgery and Its Impact on Technology-Driven Targeted Interventional and Surgical Pain Management: Perspectives from Europe, Latin America, Asia, and The United States

Can artificial intelligence algorithms recognize knee arthroplasty implants from X-ray radiographs?

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