Measurement accuracy and dependence on external influences of the iPhone X TrueDepth sensor

Breitbarth, Andreas; Schardt, Timothy; Kind, Cosima; Brinkmann, Julia; Dittrich, Paul-Gerald; Notni, Gunther

doi:10.1117/12.2530544

Cited by 27 publications

(18 citation statements)

References 2 publications

(1 reference statement)

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“…Three-dimensional face scans of each participant were captured with the following three different methods: stereophotogrammetry (3dMDtrio System; 3dMD, Atlanta, Ga) and two applications for the smartphone, taking advantage of the TrueDepth technology (iPhone Xs; Apple, Cupertino, Calif)-the Bellus3D Face Application (version 1.6.11; Bellus3D Inc, Campbell, Calif) or Capture (version 1.2.5; Standard Cyborg Inc, San Francisco, Calif). 13 The 3dMD system was chosen among the stereophotogrammetry methods because it was the most validated and reproducible. 3,14 For each patient, the 3dMD vs Bellus3D image and the 3dMD vs Capture image were compared.…”

Section: Design Of the Studymentioning

confidence: 99%

A comparison between stereophotogrammetry and smartphone structured light technology for three-dimensional face scanning

D'Ettorre¹,

Farronato²,

Candida³

et al. 2022

The Angle Orthodontist

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Objectives To compare three-dimensional facial scans obtained by stereophotogrammetry with two different applications for smartphone supporting the TrueDepth system, a structured light technology. Materials and Methods Facial scans of 40 different subjects were acquired with three different systems. The 3dMDtrio Stereophotogrammetry System (3dMD, Atlanta, Ga) was compared with a smartphone (iPhone Xs; Apple, Cupertino, Calif) equipped with the Bellus3D Face Application (version 1.6.11; Bellus3D Inc, Campbell, Calif) or Capture (version 1.2.5; Standard Cyborg Inc, San Francisco, Calif). Times of image acquisition and elaboration were recorded. The surface-to-surface deviation and the distance between 18 landmarks from 3dMD reference images to those acquired with Bellus3D or Capture were measured. Results Capturing and processing times with the smartphone applications were considerably longer than with the 3dMD system. The surface-to-surface deviation analysis between the Bellus3D and 3dMD showed an overlap percentage of 80.01% ± 5.92% and 56.62% ± 7.65% within the ranges of 1 mm and 0.5 mm discrepancy, respectively. Images from Capture showed an overlap percentage of 81.40% ± 9.59% and 56.45% ± 11.62% within the ranges of 1 mm and 0.5 mm, respectively. Conclusions The face image acquisition with the 3dMD device is fast and accurate, but bulky and expensive. The new smartphone applications combined with the TrueDepth sensors show promising results. They need more accuracy from the operator and more compliance from the patient because of the increased acquisition time. Their greatest advantages are related to cost and portability.

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Section: Design Of the Studymentioning

confidence: 99%

A comparison between stereophotogrammetry and smartphone structured light technology for three-dimensional face scanning

D'Ettorre¹,

Farronato²,

Candida³

et al. 2022

The Angle Orthodontist

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“…A total of 30,000 infrared dots are projected onto the target object and captured by an infrared-sensitive camera, allowing fine details such as contours and recesses to be detected by the camera [65]. This granularity has enabled its use in a wide range of applications, ranging from the analysis of human facial emotions to the customization of prosthetics and medical equipment requiring precise fit [63,[66][67][68][69][70][71].…”

Section: Facial Morphometrics and Malnutrition Screeningmentioning

confidence: 99%

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Tay¹,

Quek²,

Kaur³

et al. 2022

JMIR Public Health Surveill

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Undiagnosed malnutrition is a significant problem in high-income countries, which can reduce the quality of life of many individuals, particularly of older adults. Moreover, it can also inflate the costs of existing health care systems because of the many metabolic complications that it can cause. The current methods for assessing malnutrition can be cumbersome. A trained practitioner must be present to conduct an assessment, or patients must travel to facilities with specialized equipment to obtain their measurements. Therefore, digital health care is a possible way of closing this gap as it is rapidly gaining traction as a scalable means of improving efficiency in the health care system. It allows for the remote monitoring of nutritional status without requiring the physical presence of practitioners or the use of advanced medical equipment. As such, there is an increasing interest in expanding the range of digital applications to facilitate remote monitoring and management of health issues. In this study, we discuss the feasibility of a novel digital remote method for diagnosing malnutrition using facial morphometrics. Many malnutrition screening assessments include subjective assessments of the head and the face. Facial appearance is often used by clinicians as the first point of qualitative indication of health status. Hence, there may be merit in quantifying these subtle but observable changes using facial morphometrics. Modern advancements in artificial intelligence, data science, sensors, and computing technologies allow facial features to be accurately digitized, which could potentially allow these previously intuitive assessments to be quantified. This study aims to stimulate further discussion and discourse on how this emerging technology can be used to provide real-time access to nutritional status. The use of facial morphometrics extends the use of currently available technology and may provide a scalable, easily deployable solution for nutritional status to be monitored in real time. This will enable clinicians and dietitians to keep track of patients remotely and provide the necessary intervention measures as required, as well as providing health care institutions and policy makers with essential information that can be used to inform and enable targeted public health approaches within affected populations.

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“…Depth estimation using dual-camera stereoscopic images has been utilized to segment background from foreground objects and to create depth of field maps for synthetic “bokeh.” Although stereo depth estimations have good spatial resolution, they are relatively low-precision and susceptible to variability in lighting/imaging conditions. 28 Light-field imaging is an emerging alternative to stereoscopic imaging but has been evaluated in a relatively small number of studies and has not yet to be integrated in commercial systems. 29 – 31 Most recently, 3D time-of-flight cameras are an emerging capability which can potentially provide depth information at sufficiently quantitative spatial and temporal resolutions for dynamically measuring distances and volumes.…”

Section: System Interface Designmentioning

confidence: 99%

Smartphone-based imaging systems for medical applications: a critical review

2021

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. Significance: Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed. Aim: Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances. Approach: First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed. Results: The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo , multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications. Conclusions: A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forwar...

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Measurement accuracy and dependence on external influences of the iPhone X TrueDepth sensor

Cited by 27 publications

References 2 publications

A comparison between stereophotogrammetry and smartphone structured light technology for three-dimensional face scanning

A comparison between stereophotogrammetry and smartphone structured light technology for three-dimensional face scanning

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Smartphone-based imaging systems for medical applications: a critical review

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