Viability of Hand and Wrist Photogoniometry

Meals, Clifton; Saunders, Rebecca J.; Desale, Sameer; Means, Kenneth R.

doi:10.1177/1558944717702471

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…40 In manual photographic measurement, Georgeu et al compared lateral image measurement of the ring or little fingers with standard goniometry in 20 patients. 41 In their study, the average difference was 1°(SD: 6°, 95% Cl: 0°to + 2°), and the 95% LOA was −11°to + 13°, which was beyond the 42 We found our results comparable to these measurements. In our study, even if we set a ± 5°as the level of clinical acceptance, 40.6% of parameters had strictly reached the clinical agreement, and another 18.8% of parameters had almost achieved the agreement.…”

Section: Discussionsupporting

confidence: 71%

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

Fan

Wang

et al. 2023

Science Progress

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Background Telemedicine support virtual consultations and evaluations in hand surgery for patients in remote areas during the COVID-19 era. However, traditional physical examination is challenging in telemedicine and it is inconvenient to manually measure the hand range of motion (ROM) from images or videos. Here, we propose an automatic method using the hand pose estimation technique, aiming to measure the hand ROM from smartphone images. Methods Twenty-eight healthy volunteers participated in the study. An eight-hand gestures measurement protocol and the Google MediaPipe Hands were used to analyze images and calculate the ROM automatically. Manual goniometry was also performed according to the guideline of the American Medical Association. The correlation between the automatic and manual methods was analyzed by the intraclass correlation coefficient and Pearson correlation coefficient. The clinical acceptance was testified using Bland-Altman plots. Results A total of 32 parameters of each hand were measured by both methods, and 1792 measurement results were compared. The mean difference between automatic and manual methods is −2.21 ± 9.29° in the angle measurement and 0.48 ± 0.48 cm in the distance measurement. The intraclass correlation coefficient of 75% of parameters was higher than 0.75, the Pearson correlation coefficient of 84% of parameters was over 0.6, and 40.6% of parameters reached well-accepted clinical agreements. Conclusions The proposed method provides a helpful protocol for automatic hand ROM measurement based on smartphone images and the MediaPipe Hands pose estimation technique. The automatic measurement is acceptable and comparable with existing methods, showing a possible application in the telemedicine examination of hand surgery.

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Section: Discussionsupporting

confidence: 71%

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

Fan

Wang

et al. 2023

Science Progress

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“…82 For finger ROM, three studies in adults demonstrated that smartphone photography had excellent interand intra-rater reliability for MCP, PIP and DIP joint flexion and extension measurements. [83][84][85] However, only two of the three studies demonstrated high validity when compared to manual goniometry. In addition, there were no statistical differences between photo measurements of the wrist and fingers taken by patients' family members when compared to those obtained by specialist physicians.…”

Section: Rom Assessmentmentioning

confidence: 96%

The Telemedicine-Based Pediatric Examination of the Neck and Upper Limbs: A Narrative Review

Boisvert-Plante¹,

Noutsios²,

Pérez³

et al. 2021

JPR

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With the COVID-19 pandemic hastening the adoption of telemedicine into clinical practice, it has also prompted an abundance of new literature highlighting its capabilities and limitations. The purpose of this review is to summarize the current state of the literature on telemedicine applied in the context of a musculoskeletal examination of the neck and upper limbs for children 3 to 18 years old. The PubMed and ScienceDirect databases were searched for relevant articles from January 2015 to August 2021 using a combination of keywords and nested searches. General examination components including inspection, guided self-palpation, range of motion, sensory and motor examination, as well as special testing are described. Although the literature is focused mainly on adult populations, we describe how each component of the exam can be reliably incorporated into a virtual appointment specific to pediatric patients. Caregivers are generally needed for most consultations, but certain maneuvers can be self-performed by older children and adolescents alone. There is general feasibility, validity, and substantial reliability in performing most examination components of the upper limbs remotely, except for the shoulder exam. Compared to those made in person, clinical diagnoses established virtually were found to be either the same or similar in most cases, and management decisions also had high agreement. Despite this, there is evidence that some pediatric providers may not be able to collect all the information needed from a telemedicine visit to make a complete clinical assessment. Lastly, currently available smartphone applications measuring joint range of motion were found to have high reliability and validity. This narrative review not only establishes a foundation for a structured pediatric musculoskeletal examination, but also aims to increase physicians' confidence in incorporating telemedicine into their standard of care.

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“…A technique that overcomes this hindrance is photogoniometry, which has shown acceptable agreement levels both with goniometry and when tested for interobserver and intraobserver measurements. 6,7 Although photogoniometry decreases the amount of equipment required, making at-home measurement possible, static images cannot provide a full ROM evaluation. As the hand comprises numerous small joints, another potential obstacle to manual hand goniometry is the length of the process, which can take as long as 30 minutes.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

Ávila

Carter

McLeod

et al. 2021

Hand (New York, N,Y.)

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Background Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

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Viability of Hand and Wrist Photogoniometry

Abstract: At present, photogoniometry is not an acceptable alternative to manual goniometry for determining wrist and digit range of motion in general. Joint-specific photogoniometry should be the subject of future study, as should relevant imaging and software technology.

Cited by 9 publications

References 22 publications

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

The Telemedicine-Based Pediatric Examination of the Neck and Upper Limbs: A Narrative Review

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

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