Accuracy and reproducibility of a newly developed tool for volume measurements of the arm using 3D stereophotogrammetry

Verhulst, Arico C.; Wesselius, T.S.; Glas, H.H.; Vreeken, Rinaldo D.; Ulrich, Dietmar; Maal, T.J.J.

doi:10.1016/j.bjps.2017.07.016

Cited by 20 publications

(12 citation statements)

References 16 publications

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“…Three-dimensional(3D) reconstruction and modeling of hand morphology has been increasingly gaining importance for many purposes, including manufacturing of custom-made glove 1 , analysis of hand surface and volume 2 , 3 , 4 , and designing of hand surgery and humanoid robot hand 5 , 6 , 7 . Various methods of 3D imaging have been extensively applied in oral and maxillofacial surgery as well as plastic surgery 8 – 11 , because they collect surface data rapidly, non-invasively, accurately, reliably, and cost-effectively.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy of hand models obtained from two 3D scanning techniques

Yu¹,

Zeng²,

Ding³

et al. 2020

Sci Rep

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The aim of this study was to identify an efficient approach for 3D imaging of hand. The 3D photographs of hand were taken with Gemini structured-light scanning system (SL scanning) and CT scanning. The 3D photographs, average time of scanning and reconstruction were compared between these two indirect techniques. The reliability, reproducibility and accuracy were evaluated in these two indirect techniques and the direct measurement (DM). Statistical differences in the measurements were assessed by 99% probability, with clinical significance at > 0.5 mm. The Gemini structured-light scanning system established a complete and smooth 3D hand photograph with shorter scanning and reconstruction time. Reproducibility of CT scanning and SL scanning methods was better (P < 0.01, both) than the DM, but did not differ significantly from each other (P = 0.462). Of the 19 (31.58%) measurements obtained, 6 showed significant differences (P < 0.01). Significant differences were observed more often for circumference dimensions (5/9, 55.56%) than for length dimensions (1/10, 10%). Mean absolute error (AE) of the 10 subjects was very low for 3D CT (0.29 ± 0.10 mm) and SL scanning (0.30 ± 0.11 mm). Absolute percentage error (APE) was 4.69 ± 2.33% and 4.88 ± 2.22% for 3D CT and SL scanning, respectively. AE for the PIP circumference between the 3rd finger (0.58 mm) and 4th finger (0.53 mm) scan was > 0.5 mm, indicating significant difference between DM and CT scanning at the level of 99% probability. In this study, the Gemini structured-light scanning system not only successfully established a complete and smooth 3D hand photograph, but also shortened the scanning and reconstruction time. Compared to the DM, measurements obtained using the two indirect techniques did not show any statistically or clinically insignificant difference in the values of the remaining 17 of 19 measurements (89.47%). Therefore, either of the two alternative techniques could be used instead of the direct measurement method.

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Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy of hand models obtained from two 3D scanning techniques

Yu¹,

Zeng²,

Ding³

et al. 2020

Sci Rep

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“…Eight papers reported excellent ICC values for 3DCI. Pooled ICC data showed an ICCintra value of 0.99 (95% CI=0.95, 1.00) (Erends et al, 2014;Öhberg et al, 2014;Buffa et al, 2015;Hameeteman et al, 2016;Verhulst et al, 2017;Karakashian et al, 2018;Preuβ et al, 2018;Binkley et al, 2020) and an ICCinter value of 1.00 (95% CI=0.98, 0.99) (Buffa et al, 2015;Verhulst et al, 2017). The weighted mean SEM was 1.7% (=2.5%) and the weighted mean SDC was 4.7% (=6.9%) (Lu et al, 2013;Buffa et al, 2015;Landau et al, 2018;Preuβ et al, 2018;Lu & Dixon, 2019;Binkley et al, 2020).…”

Section: Reliabilitymentioning

confidence: 98%

“…Advantages of using 3DCI are that it is a simple, fast, accurate, easy to use and safe means of providing limb volumetric measurements (Öhberg et al, 2014;Buffa et al, 2015;Hoevenaren et al, 2015;Cau et al, 2016;Cau et al, 2018;Landau et al, 2018;Binkley et al, 2020). It requires minimal training to operate (Lu et al, 2014;Öhberg et al, 2014;Verhulst et al, 2017), and the single-camera 3DCI devices are portable and relatively inexpensive, especially compared to the perometer (Lu et al, 2014;Buffa et al, 2015;Cau et al, 2016;Verhulst et al, 2017;Cau et al, 2018;Binkley et al, 2020).…”

Section: Benefits and Considerations Of 3dcimentioning

confidence: 99%

“…A disadvantage of 3DCI is that not every clinic has the availability to a 3DCI system and trained staff to perform the measurement using the more complex 3DCI systems such as Vectra 3D Camera systems and 3D stereophotogrammetry (Hameeteman et al, 2016;Hoevenaren et al, 2016;Verhulst et al, 2017). Furthermore, people with limited shoulder range of movement may not be able to position their arm to accommodate this measurement.…”

Section: Benefits and Considerations Of 3dcimentioning

confidence: 99%

“…As lymphoedema is the second most feared side effect of cancer treatment among breast cancer patients (Cornelissen et al, 2018), it stands to reason that over-diagnosing lymphoedema will further increase existent distress (Alcorso & Sherman, 2015), therefore it is vital that an accurate diagnoses be made when measuring for lymphoedema. A larger cohort may present different outcomes, and with no papers (Lu et al, 2013;Öhberg et al, 2014;Lu et al, 2014;Erends et al, 2014;Buffa et al, 2015;Hoevenaren et al, 2016;Hameeteman et al, 2016;Cau et al, 2016;Levenhagen et al, 2017;Verhulst et al, 2017;Preuβ et al, 2018;Landau et al, 2018;Karakashian et al, 2018;Cau et al, 2018;Lu & Dixon, 2019;Karakashian et al, 2019;Binkley et al, 2020) reporting on 3DC's sensitivity or specificity against other methods, further research and analysis is needed.…”

Section: Establish the Accuracy Of 3dci Compared To Ctm Andmentioning

confidence: 99%

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Can 3D Camera Imaging Provide Improved Information to Assess and Manage Lymphoedema in Clinical Practice?

Cheryl¹

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Background Accurate diagnosis and measurement of limb volume in people with lymphoedema is important in order to provide best information for treatment, management and self-management. Current assessment methods lack detail and accuracy. Three-dimensional camera imaging (3DCI) holds the potential to be cheap, accurate, and provide additional material about limb shape not provided by current methods. However, there is a need to ensure that this assessment method is valid and reliable. Methodology This prospective, observational, longitudinal study utilised a diagnostic test study framework to determine the validity, reliability and accuracy of 3DCI compared to circumferential tape measurement (CTM) and perometry and to explore whether shape is a feasible alternative to measure upper limb lymphoedema. Twenty women with breast cancer-related lymphoedema were recruited. Phase one assessed criterion validity, intra-rater reliability, and accuracy of 3DCI by measuring limb volume of each participant with CTM, perometry and 3DCI four times over six months. Phase two investigated the use of limb shape as a method of lymphoedema assessment using oedema maps and calculations of shape redundancy derived from the 3DCI images in phase one. These data sets were matched against limb volume to determine criterion validity, intra-rater reliability and accuracy. Results 3DCI had high intra-rater correlation (ICC=0.87; p<0.00). Concurrent validity ranged from 0.82 to 0.86 against perometry and CTM, with good sensitivity (91.7% to 100%) and moderate specificity (50% to 66.7%). Limb shape calculation (shape redundancy) had moderate intra-rater correlation (ICC=0.71; p=0.01); but correlated poorly with limb volume (r=0.19 to 0.39). Coloured oedema maps were sensitive to change over time with colours clearly identifying problem areas and fluctuations within the affected limb. Conclusion Our study shows that 3DCI is a reliable, valid and accurate method of limb volume measurement, and that it could provide supportive information in clinical assessment. In addition, limb shape provides insight into localised areas of swelling, which other methods of lymphoedema measurement do not. However, shape redundancy requires further refinement.

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Evolutionary ensemble feature selection learning for image‐based assessment of lymphedema arm volume

Notash

Bayat

Haghighat

et al. 2021

Concurrency and Computation

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Summary Arm lymphedema is a side effect of surgery and radiation therapy in patients with breast cancer. There are several methods to measure lymphedema arm volume, such as water displacement (WD) and circumferential measurement (CM). Although these techniques have a satisfactory level of accuracy, they have some limitations for clinical use in practical applications. To overcome these drawbacks, a horizontal‐vertical image scanning (HVIS) tool, is presented as a valid method for arm volume measurement. To predict the arm volume, an evolutionary ensemble feature selection learning (named EEFSL) comprising different base learners (KNN, SVM, ANFIS, Decision Tree, and Naïve Bayes) is presented. In order to improve the performance of the EEFSL model, its hyperparameters are optimized using genetic algorithm (GA). The hyperparameters include the weights of different base learners and the specific features for each base learner. The fitness function of GA is to maximize the correlation between predicted and actual arm volumes. The proposed method was successfully developed for arm measurement of 60 arms from 30 women with arm lymphedema. Obtained results indicate that the proposed HVIS‐EEFSL method is a reliable and valid technique to measure arm volume of patients with lymphedema, suitable for daily clinical use instead of WD and CM.

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Accuracy and reproducibility of a newly developed tool for volume measurements of the arm using 3D stereophotogrammetry

Cited by 20 publications

References 16 publications

Evaluating the accuracy of hand models obtained from two 3D scanning techniques

Evaluating the accuracy of hand models obtained from two 3D scanning techniques

Can 3D Camera Imaging Provide Improved Information to Assess and Manage Lymphoedema in Clinical Practice?

Evolutionary ensemble feature selection learning for image‐based assessment of lymphedema arm volume

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