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JKM > Volume 42(1); 2021 > Article
Dong-Joo, Choi, Jo, Ha, Choi, Park, Lee, Keum, Sung, and Kim: Review Study on the Measurement Tools of Scoliosis: Mainly on Non-radiological Methods

Abstract

Objectives

The purpose of this study is to investigate the characteristics, validity, and reliability of non-radiological assessment tools of scoliosis that have been studied so far.

Methods

Electronic databases including Pubmed, Cochrane Library, CNKI, Science On, RISS, OASIS were searched by keywords including ‘scoliosis assessment’, ‘scoliosis screening’, ‘physical examination’, ‘functional measurement’, ‘photography’, and ‘smartphone’.

Results

32 articles using radiation-free assessments were identified from 1,011 records. The mostly used non-radiological methods were Surface topography, Scoliometer, Ultrasound, Digital Infrared Thermal Imaging, and Photography. The other methods were Gait analysis, 3D depth sensor imaging, and Low intensity electromagnetic scan.

Conclusions

It was found that non-radiological assessment tools might reduce the number of radiographs taken in scoliosis patients. To increase the reliability and validity, further research on the measurement tools of scoliosis will be needed.

Fig. 1
Study selection PRISMA flow chart
jkm-42-1-75f1.gif
Table 1
Characteristics of Included studies (n=32)
Variables Category n(%)
Publication year 2003~2011 7(21.9)
2012~2020 25(78.1)

Country Asia 17(53.1)
Europe 7(21.9)
North America 3(9.4)
South America 3(9.4)
Other* 2(6.3)

Study design Cross-section study 21(65.6)
Prospective study 6(18.8)
Retrospective study 3(9.4)
Observational study 1(3.1)
Experiment study 1(3.1)

* Israel, Austrailia

Table 2
Summary of the included studies. Table summarizes non-radiographic methods, Reference methods, Study design, Patients demographics and Method
Study Non-radiographic Methods Reference methods Study Design Demographicsa Methodology Description
Driscoll 201433) Scolioscreen-smartphone Smartphone, Scoliometer Cross-sectional IS patients
n= 39 (8:31)
age= 16 (SD: 1.4)
3 raters on Scolioscreen-smartphone, smartphone alone and scoliometer

Franko 201234) Scoliometer Smartphone App Scoliometer Cross-sectional Examined 60 times with both Scoliometer and Scoliguage app

Jimbo 202035) Hand-held roller(i-Scolioroller) combined with iPod touch Radiography Cross-sectional Experiment I
Plater torsos of IS patients
n= 10 (1:9)
age: 10 – 14
Experiment II
IS patients
n= 112 (15:97)
age: 6 – 17
Experiment I
3 raters repeated 4 times for inter-observer reliability
8 raters measured once for intra-observer reliability
Experiment II
A single screening examination with i-Scrolioroller by one of 3 raters

Prowse 201736) Body Level/Scoliosis meter Scoliometer, Radiographs Observational IS patients (mean Cobb’s angle= 25)
n= 31 (4:27)
age= 13.6 (SD: 0.6)
2 raters in 1 day

Sapkas 200337) Scoliometer Radiography(Cobb’s angle, Risser stage) Prospective IS patients
n= 291(47:244)
age= 14.1 (SD: 2.67)
1 rater on thoracic, thoracolumbar, lumbar regions

Coelho 201338) Scoliometer Radiography Cross-sectional IS patients
n=32
age= 18.2 (SD: 3.9)
Healthy subjects(control)
n= 32
age= 21.1 (SD: 2.2)
2 raters on 3 occasions

Bae 200423) D.I.T.I X-ray Cross-sectional Case study Patients with SP or LBP
n= 5 (5:0)
age= 38.8
Compared D.I.T.I and Cobb’s angle

Yang 201621) D.I.T.I X-ray Cross-sectional AIS Patients
n= 56 (27:29)
age= 13 (SD: 2.13)
Checked the thermal difference of 6 acupoints (GB21, BL13, BL15, BL20, BL23, BL26)

Kwok 201739) Infrared Thermography Cross-sectional Non-scoliotic students
n= 14(0:14)
age= 10 – 13
Scoliotic students
n= 17 (0:17)
age= 10 – 13
An examiner pin-pointed the body landmarks on subjects and performed Infrared imaging

Sato 202040) Digital Moiré X-ray Cross-sectional AIS patients
n= 125 (18:107)
age= 13.2 (SD: 2.2)
Categorized the results of DM into 4 classes and compared with Cobb’s angle
Yamamoto 201541) Moiré Topography Retrospective cross-sectional public school children
n= 195,149 (100,103:95,046)
age= 11–14
Reviewed the Nara City School scoliosis screening results from 1990 to 2012
Kuroki 201842) Moiré Topography Retrospective n= 689,293 (351,680:337,613)
age= 11, 14
Reviewed the SSS in Miyazaki, Japan from 1981 to 2013
Choi 200543) Surface topography X-ray Cross-sectional IS patients
n= 16 (5:11)
age= 21.7 (SD: 5.6)
Scan was performed after marking C7, S3, lower angle of scapula, acromion, iliac crest

Chowanska 201226) Surface topography Scoliometer Cross-sectional Girls between age of 9 – 13
n= 996
age= 11.0 (SD: 1.0)
1 rater on the same day with both measurements

Komeili 201444) Surface topography X-ray Prospective AIS patients
n= 46
age: 10 – 18
Healthy subjects
n= 5
8 observers classified patients based on surface topography asymmetry maps

Pino-Almero 201745) Surface topography X-ray Optical Cross-sectional AIS patients
n= 88 (12:76)
age= 13.15 (SD: 1.96)
Axial plane(DHOPI), coronal plane(POSTI), profile plane(PC) were evaluated by ST

Pino-Almero 201627) Surface topography Radiography Prospective IS patients
n= 31 (4:27)
age= 13 (range: 7 – 17)
Assessed twice at intervals of 6 months to 1 year with both measurements. DHOPI,POSTI,PC were evaluated by ST

L.Schulte 200846) Raster stereography Radiography Retrospective Long-term follow-up study IS patients
n= 16 (7:9)
age= 13 (range: 7–21)
Patients were examined using both measurements within 1 hour

Drzal-Grabiec 201447) Photogrammetric method + Mora projection Cross-sectional Randomly selected primary school children
n= 120 (50:70)
age: 7 – 11
Each subject tested twice by 2 raters with 20 min interval

Zhang 201730) Ultrasound imaging Cobb’s angle Experimental study 3 raters on 2 occasions with thoracic spine phantom set in 24 different poses

Wang 201548) Ultrasound imaging MRI Cross-sectional Female subjects with AIS
n= 16
age= 15.4 (SD: 2.6)
2 raters and each with 3 scans, took less than 1 minute per scan

Cheung 201549) Ultrasound imaging X-ray Cross-sectional Subjects with scoliosis
n= 36 (12:24)
age= 30.1 (SD: 14.5)
2 observers measured twice using both VPI-SP, VPI-TP methods

Jiang 201950) Fast 3d ultrasound projection imaging (FUPI) Volume projection imaging (VPI) method Cross-sectional Subjects with scoliosis
n= 70
age= 15.6 (SD: 2.8)
2 raters each with 2 scans, intervals of 5 minute per scan

Zheng 201651) 3D Ultrasound imaging Radiography Prospective Subjects with scoliosis
n= 49 (15:34)
age= 15.8 (SD: 2.7)
2 operators scanned twice by each, and scanned image was viewed by 3 raters. Each image was measured twice by each rater

Kang 201229) Ultrasound imaging Formetric 4D Cross-sectional AIS patients
n= 22 (8:14)
age= 15
2 raters on 2 occasions

Matamalas 201452) Digital Photography X-ray Cross-sectional IS patients
n= 80 (12:68)
age= 20.3 (SD: 8.6)
3 evaluators on 2 separate occasions, one week apart

Aroeira 201153) Computerized photogrammetry Radiography Cross-sectional IS patients
n= 16 (2:14)
age= 21.4 (SD: 6.1)
2 raters with both measurements

Saad 200954) Photogrammetry Radiography Cross-sectional Prospective Scoliosis patients
n= 40 (8:32)
age= 23.4 (SD:11.2)
Subjects were photographed by 2 examiners and 15 days later, photographed again by the first examiner

Kim 201455) Gait parameter X-ray Cross-sectional Normal group
n= 20
age= 11.9 (SD: 0.2)
AIS group
n= 20
age= 11.3 (SD: 0.6)
Assessed weight-bearing distribution, stance/swing phase and gait velocity

Cho 201856) Machine learning based gait analysis test X-ray Cross-sectional Teenage scoliosis patients
n= 24
age= 15.2 (SD: 2.5)
Normal subjects(control)
n= 18
age= 15.7 (SD: 2.6)
All subjects completed a 10m gait course for 10 times

Ovadia 200757) Low intensity electromagnetic scan sensoring the spatial position of spinous process X-ray Prospective AIS patients
n= 124 (35:89)
age= 13 (SD: 3.17)
4 independent sites, 6 independent examiners, repeated twice for each patient

Kokabu 201958) 3-D depth sensor imaging system Radiography Prospective cohort study Subjects with suspected AIS
n= 170 (21:149)
age= 14.3 (range: 8 – 18)
Subjects back was scanned by a 3D depth sensor

IS Idiopathic Scoliosis

a n = sample size of study (Male:Female), Mean age is reported with the standard deviation in the parentheses.

SP Shoulder Pain, LBP Lower Back Pain, D.I.T.I Digital Infrared Themographic Imaging, AIS Adolescent Idiopathic Scoliosis, DM Digital Moiré, SSS school scoliosis screening

DHOPI horizontal plane deformity index, POSTI posterior trunk symmetry index, PC columnar profile

VPI-SP volume projection imaging-spinous process, VPI-TP volume projection imaging-transverse process,

Table 3
Validity and reliability of the included studies
Large category of assessment tools Study Validity Inter-rater reliability Intra-rater reliability
Scoliometer Driscoll 201433) Scolio-smartphone ICC= 0.89 Scolio-smartphone ICC= 0.94
Franko 201234) Pearson’s Correlation Coefficient= 0.9995 (p<0.001)
Jimbo 202035) ATI ICC= 0.733
Sum ATI ICC= 0.745
ATI ICC of orthopedic doctor/office worker/assistant technical expert= 0.851, 0.786, 0.772
Sum ATI ICC of orthopedic doctor/office worker/assistant technical expert= 0.856, 0.900, 0.796
Prowse 201736) Good correlation with the Scoliometer (rho= 0.78)
Moderate correlation for ATR (rho= 0.627)
Thoracic kyphosis ICC= 0.94
ATR ICC= 0.92
Lumbar lordosis ICC= 0.79
Cervical lordosis= 0.51
measured by the Baseline® Body Level/ Scoliosis meter (Examiner A ICC3,3= 1.00, Examiner B= 0.98)
measured by the Scoliometer (Examiner A ICC3,3= 0.99, Examiner B= 0.98)
Sapkas 200337) Thoracic r= 0.685 (p<0.01)
Thoracolumbar r= 0.572 (p<0.01)
Lumbar r= 0.677 (p<0.01)
Coelho 201338) r= 0.7 (p<0.05) 0.89 0.92
D.I.T.I Kwok 201739) ICC values > 0.9
Surface topography Sato 202040) κ coefficient 0.70, p<0.001 κ coefficient 0.73, p<0.001
Choi 200543) Correlation between CA and DHCT(difference of height cervicothoracic) r= 0.591, p<0.05
Correlation between CA and DHT(difference of height thoracic) r= 0.768, p<0.01
Correlation between CA and DHTL(difference of height thoracolumbar) r= 0.704, p<0.01
Correlation between CA and DHL(difference of height lumbar)
r= 0.509, p<0.05
Chowanska 201226) Inter-observer error: 0.8° Intra-observer error for STR parameter: 1.9°
Komeili 201444) Multi-observer κ value: 0.62 Mean κ coefficient: 0.85
Pino-Almero 201745) CA with DHOPI/POSTI: r= 0.810, 0.629
PC variables with thoracic kyphosis angle/lordosis lumbar angle: r=0.453, 0.275
Pino-Almero 201627) CA with DHOPI: r= 0.720, p < 0.01
CA with POTSI: r= 0.753, p < 0.01
DHOPI: 0.987
POSTI: 0.978
PC: 0.969 (p<0.05)
DHOPI: 0.983
POSTI: 0.959
PC: 0.984
L.Schulte 200846) Cobb/Rsg
Lateral deviation (Max/RMS) R2 = 0.8, 0.7
Rotation (Max/RMS) R2 = 0.6, 0.5
Cobb/Rad
Lateral deviation (Max/RMS) R2 = 0.9, 0.8
Rotation (Max/RMS) R2 = 0.6, 0.6
Rsg/Rad
Lateral deviation (Max/RMS) R2 = 0.8, 0.7
Rotation (Max/RMS) R2 = 0.6, 0.5
Ultrasound imaging Zhang 201730) 2D deformity r= 0.92
3D deformity r=0.78
Wang 201548) Cobb’s angle degrees (10,2~68.2°/10.0~20.0°/20.0~45.0°)
r= 0.997, 0.934, 0.989
Variation in selected UEV (variation 0/1/2)
r= 0.998, 0.998, 0.997
Variation in selected LEV (variation 0/1/2)
r= 0.997, 0.998, 0.989
Level of apical vertebra (T1–T4/T5–T8/T9–T12/L1–L5)
r= 0.987, 0.997, 0.999, 0.997
Ultrasound ICC= 0.995
Ultrasound SEM= 1.0°
MRI ICC= 0.997 –0.995
MRI SEM= 1.0°
Ultrasound ICC= 0.933 – 0.997
Ultrasound SEM= 0.6 – 0.8°
MRI ICC= 0.997 – 0.998
MRI SEM= 0.5 – 0.7°
Cheung 201549) VPI-SP R2= 0.79 (p<0.001)
VPI-TP R2= 0.78 (p<0.001)
VPI-SP ICC: 0.92 (p<0.001)
VPI-TP ICC: 0.96 (p<0.001)
VPI-SP ICC: 0.99 (p<0.001)
VPI-TP ICC: 0.98 (p<0.001)
Jiang 201950) r= 0.954
Bland-Altman plot (D=0.1)
ICC= 0.90 ICC= 0.96
Zheng 201651) Thoracic region R2= 0.78
Lumbar region R2= 0.72
ICC= 0.88 – 0.93 (0.90 ± 0.02)
Inter-operator reliability for scanning using Scolioscan ICC= 0.87 – 0.94 (0.92 ± 0.03)
ICC= 0.94 – 0.99 (0.97 ± 0.02)
Intra-operator reliability for scanning using Scolioscan ICC= 0.88 – 0.97 (0.94 ± 0.03)
Kang 201229) Trapezius Rt: ICC=.866, Lt: ICC=.820
Rhomboid Rt: ICC=.859, Lt: ICC =.920
Posterior serratus muscles Rt: ICC=.578, Lt: ICC=.789
Total thicknesses Rt: ICC=.933, Lt: ICC=.967
ICC= 0.872 – 0.958
Photography Matamalas 201452) 0.37 < r < 0.51 Back (LRTA/SHA/AHA) ICC= 0.80, 0.80, 0.88
Front (LRTA/SHA/AHA) ICC= 0.65, 0.89, 0.85
Back (LRTA/SHA/AHA) ICC= 0.79, 0.88, 0.93
Front (LRTA/SHA/AHA) ICC= 0.78, 0.91, 0.91
Aroeira 201153) Thoracic region Kappa index= 0.92
Lumbar region Kappa imnex= 0.82
Saad 200954) Thoracic curves R= 0.619
Lumbar curves R= 0.551
Thoracic: 0.942 (p<0.001)
Lumbar: 0.564 (p= 0.010)
Thoracolumbar: 0.879 (p<0.001)
Thoracic: 0.963 (p<0.001)
Lumbar: 0.975 (p<0.001)
Thoracolumbar: 0.945 (p<0.001)
Gait analysis Cho 201856) Cross-validation test result
Accuracy of SVM to recognize scoliosis group and control group: 90.5% (if optimally selected, 95.2%)
Accuracy of SVM to recognize scoliosis severity gait patterns: 81.0% (if optimally selected, 85.7%)
Low intensity electromagnetic scan Ovadia 200757) Thoracic r= 0.87
Lumbar r= 0.84
All curves r= 0.86 (p < 0.0001)
mean absolute difference between the paired coronal measurements: 6.3° (SD = 4.9°), Pearson’s correlation coefficient: 0.86 (P= 0.08)
mean absolute difference between the paired sagittal measurements: 6.1° (SD = 4.9°), Pearson’s correlation coefficient: 0.87 (P= 0.11)
mean absolute difference between the paired coronal measurements: 2.74° (SD = 2.4°), Pearson’s correlation coefficient: 0.86 (P= 0.11)
mean absolute difference between the paired sagittal measurements: 4.83° (SD = 4.28°), Pearson’s correlation coefficient: 0.87 (P= 0.67)
3-D depth sensor imaging system Kokabu 201958) r= 0.85 (p<0.01)

ICC Intra-class correlation coefficient, ATI Angle of trunk inclination, ATR Axial thoracic rotation

CA Cobb’s angle, DHOPI horizontal plane deformity index, POSTI posterior trunk symmetry index, PC columnar profile, RSG raster stereography, Max maximum, RMS root mean square, Rad radiography

SEM standard error of measurement, UEV upper-end vertebra, LEV lower-end vertebra, VPI-SP Volume projection imaging spinous process method, VPI-TP Volume projection imaging transverse process method

LRTA left/right trapezium angle ratio, SHA shoulder height angle, AHA axilla height angle, SVM support vector machine

Table 4
Methodological characteristics of included studies, summarizing equipments, study aims and conclusion
Study Equipments required Aims/rationale Conclusions Authors’ consensus (Yes/No)
Driscoll 201433) Smartphone(iPhone4), Scoliguage app, Scolioscreen and Scoliometer To compare the reliability and accuracy of a Scolioscreen-smartphone combination, a smartphone alone, and a Scoliometer The Scolioscreen-smartphone provides a reliability and consistency similar to the gold standard (use of Scoliometer by spine surgeon). Yes
Franko 201234) Scoliguage app, iPhone 4s device with iOS 5 installed To demonstrate equivalence between the scoligauage iPhone application and a standard Scoliometer read by various providers. The Scoliguage app is a novel tool that replicates the function of a standard scoliometer but with a lower cost and greater convenience. Yes
Jimbo 202035) i-Scolioroller, iPod touch(Apple Inc., Cupertino, CA) To evaluate the intra-and inter-observer reliability of the i-Scolioroller and to determine the optimal cutoff values of i-Scolioroller measurements I-Scolioroller has a sufficiently high sensitivity for detecting adolescent scoliosis with a Cobb’s angle ≥20°. (Sensitivity: 88.9%, Specificity: 62.5%) Yes
Prowse 201736) Baseline® Body Level/Scoliosis meter (Orthopaedics Systems Incorporation®) To investigate the reliability and validity of the Baseline® Body Level/Scoliosis meter for AIS The Baseline® Body Level/Scoliosis meter provides reliable transverse and sagittal cervical, thoracic and lumbar measurements and valid transverse plan measurements of mild-moderate scoliosis deformity. Yes
Sapkas 200337) Scoliometer (Orthopaedic Systems Inc, Hayward, CA) To create mathematic formulas that could predict the Cobb’s angle using the scoliometer measurements Scoliometer values combined with three mathematical formulas permit assessment of adolescent idiopathic scoliosis and follow-up for progression of the deformity Yes
Coelho 201338) Scoliometer (Orthopaedic Systems Inc, Hayward, CA) To measure intra- and interrater reliability, sensitivity and specificity of the scoliometer Scoliometer and radiographic measurements showed good correlation. (The highest sensitivity value= 0.87 at 5° trunk rotation) Yes
Bae 200423) Digital Infrared Thermographic Imaging (IRIS5000, MEDICORE, SEOUL, KOREA) To report that D.I.T.I can be used for diagnosis of scoliosis Results suggest that D.I.T.I can explain physiologic and functional abnormalities than X-ray, in diagnosis of scoliosis No
Yang 201621) Digital Infrared Thermographic Imaging (T-1000 HD, MESHMEDICAL, Korea) To investigate the correlation between Cobb’s angle and D.I.T.I on AIS Using D.I.T.I, acupoint Simsu(BL15) is expected to be a valid indicator for the diagnosis and treatment of AIS No
Kwok 201739) FLIR E33 camera (FOL-18 lens; 10,800 pixels), Thermacam Researcher Professional 2.9 Software(FLIR) To explore the possibility of using IR thermography to evaluate Infra red emissions from subjects to detect abnormalities in temperature distribution in their paraspinal muscles. The findings of this study suggest the feasibility of incorporating IR thermography as part of SSS. Yes
Sato 202040) Hump measurement system with a built-in 3D camera, personal computer (Kinect for Windows: Microsoft Corporation, Redmond, Washington) To assess the usefulness of Digital Moiré(DM) for scoliosis screening. DM is useful as a new method for the screening of scoliosis with sufficient accuracy and reliability to replace Moiré topography. (Sensitivity= 0.98, Specificity= 0.53) Yes
Yamamoto 201541) Evaluate the accuracy of Moiré topography tool as a screening tool. Moiré topography had a high false-positive rate (66.7%), which did not improve with examiner experience. No
Kuroki 201842) To make clear the both results and problems of SSS by Moiré topography(MT) SSS by MT seemed to be effective in detecting scoliosis although both positive predictive value and the reference rate to the second screening were low. Yes
Choi 200543) 3D-surface topography (Koastron, IBS-2000, Korea) To measure correlation between 3D-surface topography and Cobb’s angle in scoliosis 3D surface topography and Cobb’s angle was highly correlated. Yes
Chowanska 201226) CQ Electric System (Poland) device To assess the usefulness of Surface topography(ST) for scoliosis screening Did not reveal the advantage of ST as a scoliosis screening method in comparison with the use of scoliometer. No
Komeili 201444) Four VIVID 910 3D laser scanners (Konica Minolta Sensing Inc., Ramsey, NJ, USA), Polygon Editing Tool (PET version 2.21, Konica Minolta), Geomagic software (Geomagic Studio 12, Morrisville, NC, USA) Introduces a 3D markerless analysis technique for assessing torso asymmetry in AIS and a system for classifying patients based on this technique. Distinctive patterns of asymmetry were identified with very good to excellent reliability. Yes
Pino-Almero 201745) Mobile white screen, projector, digital camera, computer with image recognition software designed in Matlab 7.9.0 (Matlab & Simulink Release 2009b. The Mathworks, Inc., Natick, MA, USA) To study the correlation between asymmetry of back (measured by ST) and deformity of the spine (quantified by Cobb’s angle) ST cannot substitute for radiographs in the diagnosis of scoliosis but it can offer data that complement radiologic study. No
Pino-Almero 201627) Mobile white screen, projector EPSON(3LCD projector model: EMP-835), digital camera CANON, computer(MacBook Pro) with the program developed in MATLAB 7.9.0 To study if ST would be useful in the follow-up of AIS patients ST showed 90.32% agreement with radiographic method in follow-up of AIS patients. Yes
L.Schulte 200846) Formetric system (Diers International, Wiesbaden, Germany) To investigate the reliability and accuracy of raster stereography in comparison with radiography as the gold standard. Rasterstereography accurately reflects the radiographically measured progression of scoliosis during the long-term follow-up, but these parameters are not directly comparable with the Cobb’s angle. Yes
Drzal-Grabiec 201447) Mora projection (MORA System 4th Generation) To evaluate the compatibility of external measurements of parameters characterizing scoliosis using the photogrammetric method. The photogrammetric method gives significant results in terms of parameters characterizing the position of the shoulder blades and shoulders, as well as pelvis rotation. Yes
Zhang 201730) Ultrasound scannerDP-50 (Mindray Ltd., China) with a MHz probe, electromagnetic spatial sensing device PATRIOT (Polhemus Ltd., USA), personal computer (Intel Xeon CPU E5-1620-v3, 3.5GHz with 8G RAM) To analyze the correlation between the Cobb’s angle and spinous process angle(SPA) measured by ultrasound data SPA and Cobb’s angle has high correlation especially for the curves with 2D deformity. Yes
Wang 201548) 3-D SonixTABLET, SonixGPS, C5-2/60 Convex transducer (Ultrasonix, Canada) To evaluate validity and reliability of Ultrasound imaging compare to MRI The ultrasound imaging is a reliable and valid measurement of spinal curvature in the coronal plane using Center of Laminae (COL) method. Yes
Cheung 201549) Ultrasound scanner EUB-8500 (Hitachi Ltd., Tokyo, Japan), a computer with an Intel Core 2 Q6600 2.4-GHz processor and a video capture card NIIMAQPCI/PXI-1411 (National Instruments Corporation, Austin, TX, USA), a compact electromagnetic spatial sensing device MiniBird Model 130 (Ascension Technology Corporation, Burlington, VT, USA). To assess the performance of newly developed freehand 3D ultrasound system. Results suggested that the ultrasound volume projection imaging method can be a promising approach for the assessment of scoliosis. Yes
Jiang 201950) A custom-designed liner 2-D ultrasound probe (width: 10 cm; frequency: 4–10 MHz), An electromagnetic spatial sensing device (driveBAY, Ascension Technology Corporation, Burlington, USA) To develop a fast 3-D ultrasound projection imaging (FUPI) method for assessment of scoliosis. The results indicate that the developed projection imaging method could greatly decrease the processing time while preserving the comparative image quality. Yes
Zheng 201651) The Scolioscan system (Model SCN801, Telefield Medical Imaging Ltd, Hong Kong) To test the reliability of spine deformity measurement of Scolioscan and its validity compared to the Cobb’s angle from radiography in AIS patients. Scolioscan is reliable for measuring coronal deformity for patients with AIS and appears promising in screening large numbers of patients, for progress monitoring, and evaluation of treatment outcomes. Yes
Kang 201229) Sonoace 8000 (Medison Inc, South Korea) To demonstrate the reliability of using diagnostic ultrasound imaging(USI) in the assessment of the thickness of the soft tissues of the interscapular region in AIS USI could be a reliable method in evaluating of the thickness of the soft tissues of the interscapular region which in turn could be a useful guide to the assessment of the effects of AIS. Yes
Matmalas 201452) Digital Nikon D5100 (Nikon Corporation, Tokyo, Japan) camera To determine the validity of digital photography as an evaluation method for shoulder balance (ShB) in patients with idiopathic scoliosis. Digital clinical photography appears to be a reliable method for objective clinical measurement of ShB. The correlation between clinical and radiological balance is statistically significant although moderate/weak. No
Aroeira 201153) A digital camera, Sony 7.1 megapixel (Sony, Manaus, Amazonas, Brazil), a Greika WT3750 (Greika, São Paulo, SP, Brazil) tripod, A Carci Simetograph (Carci, Americanópolis, SP, Brazil), CorelDraw13 software (CorelCorporation, Ottawa, Canada) To develop a protocol for computerized photogrammetry for the quantification of scoliosis, and to mathematically relate this proposed method with the Cobb radiographic method. The preliminary results presented demonstrate equivalence between the two methods. More studies are needed. Yes
Saad 200954) Photographic camera positioning (Sony P200 7.2.mp; Sony, Tokyo, Japan) The purpose of this study was to investigate the reliability and validity of photogrammetry in measuring the lateral spinal inclination angles. Although the current study did not show the validity of photogrammetry as a measure of the lateral spinal curvature in scoliosis, high reliability coefficients were observed. No
Kim 201455) The Smartstep™ pneumatic insole, The Smartstep™ software To demonstrate that relationship between scoliosis and gait factor and foot weight bearing in ambulation. In this study Influence of scoliosis was not found on the rate of stance phase and rate of swing phase and gait velocity. Fore foot weight bearing (P = 0.019) was significantly higher in the AIS group. No
Cho 201856) IMU-based system (Human Track, Rbiotech Co., Ltd., Seoul, Korea) consisting of a gyroscope, accelerometer and magnetic sensor This study discussed application of a machine learning approach for the automatic cognition of gait changes due to scoliosis using gait measures. Study’s results demonstrate considerable potential in applying SVMs in gait classification for medical applications. (Accuracy of SVM to recognize scoliosis group and control group: 90.5% ) (Accuracy of SVM to recognize scoliosis severity gait patterns: 81.0% ) Yes
Ovadia 200757) Ortelius800™ system (OrthoScan Technologies, Rosh Pina, Israel) To investigate the clinical value of Ortelius800™ Found the novel clinical tool to be reliable for following mild and moderate idiopathic curves in both coronal and sagittal planes. Yes
Kokabu 201958) Consumer-grade 3D depth sensor (Xtion Pro Live, ASUSTeK Computer Inc. Taipei, Republic of China), a laptop computer (Core-i5, 7200U-4 GB HP pavilion-15-au105tu, HP Inc, California, USA) To report the potential accuracy of newly developed, asymmetry-recognition system for the surface of the human back using a 3D depth sensor This study demonstrates the outstanding ability of this new system for deciding whether additional radiography is needed to define scoliosis. This system can be an alternative to the forward bend test and scoliometer measurement in clinics. (Sensitivity: 0.97, Specificity: 0.93) Yes

D.I.T.I digital infrared thermographic Image, IR infrared, SSS school scoliosis screening, AIS adolescent idiopathic scoliosis

ST surface topography, AIS adolescent idiopathic scoliosis

SVM support vector machine

참고문헌

1. Hresko MT. Idiopathic scoliosis in adolescents. New England Journal of Medicine. 2013; 368:9. 834–41.
crossref

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