2D Photogrammetry Image of Scoliosis Lenke Type Classification Using Deep Learning

Sabri, Nurbaity; Hamed, Haza Nuzly Abdull; İbrahim, Zarina Bibi; Ibrahim, K.S.

doi:10.1109/icsengt.2019.8906428

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…So, the decision support system provided here is one step ahead of alternative studies by confirming accurate measurements by Deep Learning, successful outcomes in terms of diagnosis, and ensuring a patient-specific treatment planning, which has not been done yet (to the best of the authors' knowledge). • It is remarkable that studies such as [66,67] followed the Lenke method for classifying the scoliosis cases. Except from angle measurement works, such classification studies are critical for diagnosis and treatment cases.…”

Section: Discussionmentioning

confidence: 99%

“…The study differs from others by considering photogrammetry images for the classification purposes in Lenke method. In detail, the evaluations were done to determine the relationship between the different dimensions of the image data and the Lenke classification accuracy[66]. In another study with the Lenke classification, Anitha worked with Deep Learning to eliminate the observer errors[67].…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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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“…The misalignment of lumbar vertebrae is a common deformity that makes the nerve roots to be pulled or compressed, causing low back pain, sciatica, or lower extremity pain and numbness. Consequently, it is clinically necessary for orthopedists or neurosurgeons to diagnose various spinal conditions including spinal fractures, scoliosis [5,6], spinal deformity [7], degenerative spondylolisthesis [8,9], stenosis [10,11], herniated intervertebral disc (HIVD) [12,13] and degenerative osteophytes [14]. According to AO (Arbeitsgemeinschaft für Osteosynthesefragen) spine founded in 1958 in Biel-Bienne, Switzerland by a group comprising Swiss general and orthopaedic surgeons, the annual global incidence of degenerative spine disease and spondylolisthesis are 3.63% and 0.53%, respectively [15].…”

Section: Introductionmentioning

confidence: 99%

LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images

Trinh¹,

Shao²,

Hsieh³

et al. 2022

Preprint

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A common spinal condition, spondylolisthesis is the presence of a relative back or forth displacement between the upper and lower vertebra due to one vertebra being oriented away from the smooth curvature of a normal spine. Aging-related illnesses such as degenerative spondylolisthesis are especially burdensome on social welfare and health-care systems in an aging society, especially radiologists and clinical physicians. Therefore, we proposed a computer aided diagnosis algorithm, named LumbarNet, for vertebral slippage detection on clinical X-ray images. Collaborating with i) a P-grade, ii) a piecewise slope detection scheme, and iii) a dynamic shift detection routine, LumbarNet was thus specialized for analyzing complex structural patterns in lumbar spine X-ray images and outcompeted other U-Net based methods. Extensive experiments on lumbar spine X-ray images in standard clinical practices showed that LumbarNet achieved a mean intersection over union value of 0.88 in vertebral region detection and an accuracy of 88.83% in vertebral slippage detection.

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“…Bu deneyler için kırk beş görüntü üzerinde yapılmıştır. Görüntü boyutlarının değişiminin doğruluk değerlerindeki etkisi üzerinde durulmuştur[44]. Anitha çalışmasında Lenke yöntemiyle sınıflandırma işlemi üzerine çalışmıştır.…”

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Capsule Networks Based Automatic Measurement System for Scoliosis

Goral

Köse

2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Skolyoz, omurganın eğrilmesi ile birlikte omurga genel yapısını deforme eden bir hastalıktır. Skolyoz tanı ve tedavi aşamasında çeşitli yöntemler olmakla birlikte, temel amaç Cobb açısı adı verilen eğrilik açısını azaltarak Skolyoz seviyesini düşürme çerçevesinde şekillenmektedir. Cobb açısı ölçümü esasında uzman tarafından, omurga röntgen filmleri üzerinde manuel olarak gerçekleştirilmektedir. Ancak bu sürecin derin öğrenme gibi bir Yapay Zeka yaklaşımıyla otomatikleştirilmesi hem hasta hem de uzman açısından büyük kolaylık ve kesinlik sağlayacaktır. Açıklamalardan hareketle bu çalışmada, öncelikli olarak Skolyoz ve derin öğrenme odaklı çalışmalar açısından literatürün güncel durumu ele alınmış, ardından Kapsül Ağları (CapsNet) tabanlı bir çözüm ile Cobb açısı ölçümlerinin otomatik bir hale getirilmesi sağlanmıştır. CapsNet çözümünün, ConvNet, BoostNet, RFR ve ResNet-50 modelleri ile karşılaştırılması neticesinde en iyi bulguları CapsNet modelinin verdiği tespit edilmiştir.

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2D Photogrammetry Image of Scoliosis Lenke Type Classification Using Deep Learning

Cited by 8 publications

References 16 publications

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

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

LumbarNet: A Deep Learning Network for the Automated Detection of Lumbar Spondylolisthesis From X-Ray Images

Capsule Networks Based Automatic Measurement System for Scoliosis

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