A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation

Alukaev, Danis; Kiselev, Semen; Mustafaev, Tamerlan; Ainur, Ahatov; Ibragimov, Bulat; Vrtovec, Tomaž

doi:10.1007/s00586-022-07245-4

Cited by 17 publications

(3 citation statements)

References 28 publications

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“…21 Anatomic landmarks and Cobb angle measurements are also identified from CT images of the spine by determining the vertebral body shape, intervertebral disk height, thoracic kyphosis, and lumbar lordosis. 22 ML uses CT attenuation of vertebrae to identify osteoporotic patients by transforming Hounsfield units into estimations of bone mineral densities and eventually T-scores. [23][24][25] Automated diagnosis of spinal tuberculosis has also been performed.…”

Section: Ct Imagingmentioning

confidence: 99%

Machine Learning in Spine Surgery: A Narrative Review

Adida,

Legarreta,

Hudson

et al. 2023

Neurosurgery

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Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.

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Section: Ct Imagingmentioning

confidence: 99%

Machine Learning in Spine Surgery: A Narrative Review

Adida,

Legarreta,

Hudson

et al. 2023

Neurosurgery

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“…Das et al introduced a novel deep neural network architecture coined as "RIMNet", a region-toimage matching network model, and these models can automatically identify and segment intervertebral discs from multimodal magnetic resonance imaging (MRI) images. [19] Several researchers have proposed an automated DL framework based on an ensemble of U-Nets to perform vertebral morphometry and measure the Cobb angle directly on three-dimensional (3D) computed tomography (CT) images of the spine. [20] Deep learning in spinal disease diagnosis DL algorithms have been employed to diagnose a variety of spinal diseases, such as tumor, [21] infection, [21] osteoporosis, [22] scoliosis, [22] fracture [23] and degenerative disease.…”

Section: Deep Learning In Spinal Image Recognitionmentioning

confidence: 99%

The clinical application of artificial intelligence technology in spinal surgery

Feng,

Yang,

Zhang

et al. 2023

Med Robot

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Artificial intelligence (AI) is a field that investigates how to endow computers and computer-controlled machines with thecapability to imitate human intelligence. As a significant driving force for the new wave of technological revolution andindustrial transformation, AI has emerged as a research hotspot in the medical domain. The progress in AI technology hasprofoundly influenced medicine, particularly offering new opportunities for precision and automation in spinal surgery. AI, acomprehensive field, encompasses a variety of research areas such as machine learning (ML), deep learning (DL), computervision, natural language processing, and robotics, among others. These subfields intersect and potentially overlap to asignificant degree. Furthermore, robotics and AI, closely intertwined, maintain a symbiotic relationship. AI, a discipline withincomputer science, aspires to develop and implement intelligent machines, with robots exemplifying these creations inphysical form. AI equips robots with capabilities for environmental comprehension, information processing, decision-making,and learning. This review aims to examine the clinical application of AI technology in spinal surgery, with a focus ontraditional ML, DL, and robotics. We will discuss the merits and drawbacks of these technologies, as well as futuredevelopment trends.

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“…As general, the studies including decision support approach have been targeting specific findings without using complete software environments. They also need sometimes complicated data and even use of 3D image processing [29][30][31][32]. So, it would be great to ensure a more practical software use for helping physiotherapists to save their time.…”

Section: Introductionmentioning

confidence: 99%

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Goral

Köse

2022

IEEE Access

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Scoliosis is a disease caused by the spine curving. It is treatable but physiotherapists may do different measurements for curvature angles. That's a problem affecting the treatment planning. This study aims to develop a Deep Learning-based decision support system, which diagnoses scoliosis and plans treatments via Schroth method. The system has an interpretable and explainable CapsNet model processing x-ray image to detect 68-point vertebrae and make Cobb angle measurements. By using angle values and patient parameters, treatment is planned through an automated Schroth definition and the Fuzzy Logic. In the evaluations, the CapsNet had dominating findings (some of them are MSE: 0.0038, PCC: 0.93, Accuracy: 0.98). The Fuzzy Logic model was accurate at exercise plans for past cases. Also, physiotherapists and patients had positive feedback for the system usage, trustworthiness, diagnosis, treatment planning and tracking. As a conclusion, the system ensures advancements for automated diagnosis and treatment of scoliosis.

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A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation

Cited by 17 publications

References 28 publications

Machine Learning in Spine Surgery: A Narrative Review

Machine Learning in Spine Surgery: A Narrative Review

The clinical application of artificial intelligence technology in spinal surgery

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

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