Parsonage-Turner syndrome presenting with left shoulder pain and weakness: a case report

Kulaklı, Fazıl; Duman, Sencer

doi:10.18621/eurj.558457

Cited by 3 publications

(1 citation statement)

References 10 publications

(12 reference statements)

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“…In addition to this, virtually the entire work fails to incorporate deep learning and machine learning techniques and lacks a substantial dataset. 23–26…”

Section: Introductionmentioning

confidence: 99%

Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

Ramachandran

Gobalakrishnan

Chokkalingam³

2022

Proc Inst Mech Eng H

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Turner Syndrome (TS) is a chromosomal disorder, wherein the female’s growth is impacted. Immature ovaries, low stature, and heart abnormalities are a range of developmental and medical issues due to TS. The condition of TS might be detected prior to birth, throughout infancy or in the early years of life. The diagnosis of TS in girls with modest symptoms and indications is sometimes deferred until they reach adolescence or become young adults. This study presents an algorithm to segment the hand digital X-ray image in children with TS. In medical image and computer vision examination, image segmentation is demanding, and very crucial. Prevailing segmentation algorithms even now suffer from common segmentation issues including under-segmentation, over-segmentation, and spurious or non-closed edges, regardless of the several years of studies. In this paper, Anchor Based Link (ABL) segmentation approach is proposed to detect TS based on fourth Metacarpal bone from left hand X-ray images. The detection of TS is demonstrated based upon the comparison of proposed approach with existing watershed segmentation and Gaussian-Mixture-Model-based Hidden-Markov-Random-Field (GMM-HMRF) method. The proposed approach attains better segmentation based on the ratio of height and width of left fourth finger that is analyzed for normal children and children having TS with the help of edge pixel present in the metacarpal bone that has been segmented. The suggested method is verified on fifty (50) sample X-ray hand images of carpal bones, providing 0.60 ± 0.02 as an average Dice coefficient.

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“…In addition to this, virtually the entire work fails to incorporate deep learning and machine learning techniques and lacks a substantial dataset. 23–26…”

Section: Introductionmentioning

confidence: 99%

Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

Ramachandran

Gobalakrishnan

Chokkalingam³

2022

Proc Inst Mech Eng H

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An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods

et al. 2020

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This research proposes to use ensemble learning methods to diagnose and predict Turner syndrome using facial images. Turner syndrome, also known as congenital ovarian hypoplasia syndrome, is a common clinical chromosomal disorder. Without the aid of cytogenetic diagnostic results, the accuracy of diagnosis made by the paediatrician is unsatisfactory. Early diagnosis of the Turner syndrome requires the expertise of well-trained medical professionals, which may hinder early intervention due to a high potential cost. So far, most of the studies have reported the use of clinical chromosome detection to diagnose Turner syndrome. In this research, we are the first to use facial recognition technology to diagnose Turner syndrome using ensemble learning techniques. First, the features from each of the facial image are extracted by principal component analysis, kernel-based principal component analysis, and others. Second, we randomly selected samples and features to establish a basic learning model. Finally, we developed a combination of multiple basic learning models using majority voting and stacking for the facial image classification task. Experimental results show that the correct classification rate of the Turner syndrome detection was elevated up to 88.1%. The proposed method can be implemented to automatically diagnosis Turner syndrome patients that can facilitate clinicians during the prognosis process.

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Automated Approach to Detect and Monitor the Development of Turner’s Syndrome

Ramachandran

Gobalakrishnan

Chokkalingam³

2022

2022 Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Parsonage-Turner syndrome presenting with left shoulder pain and weakness: a case report

Cited by 3 publications

References 10 publications

Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

Detection of Turner syndrome using hand X-ray using anchor based links segmentation method

An Automated Approach to Diagnose Turner Syndrome Using Ensemble Learning Methods

Automated Approach to Detect and Monitor the Development of Turner’s Syndrome

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