A 3D total-body photography research network: the Australian experiment

Rutjes, Chantal; Torrano, Joachim; Soyer, H. Peter

doi:10.1007/s00105-021-04938-7

Cited by 16 publications

(12 citation statements)

References 13 publications

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“…These body scanners offer a fast and objective way of obtaining skin findings and can be used for example for automated scoring of skin diseases. 30 It was shown that involvement of a sensitive body area, like genitals, leads to decreased quality of life in dermatological conditions. 28 Therefore, the affected body part needs to be analyzed and included in scores for dermatological conditions to get the most accurate representation of disease severity.…”

Section: Discussionmentioning

confidence: 99%

Automatic body part identification in real‐world clinical dermatological images using machine learning

Sitaru,

Oueslati,

Schielein

et al. 2023

J Deutsche Derma Gesell

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SummaryBackgroundDermatological conditions are prevalent across all population sub‐groups. The affected body part is of importance to their diagnosis, therapy, and research. The automatic identification of body parts in dermatological clinical pictures could therefore improve clinical care by providing additional information for clinical decision‐making algorithms, discovering hard‐to‐treat areas, and research by identifying new patterns of disease.Patients and MethodsIn this study, we used 6,219 labelled dermatological images from our clinical database, which were used to train and validate a convolutional neural network. As a use case, qualitative heatmaps for the body part distribution in common dermatological conditions was generated using this system.ResultsThe algorithm reached a mean balanced accuracy of 89% (range 74.8%–96.5%). Non‐melanoma skin cancer photos were mostly of the face and torso, while hotspots of eczema and psoriasis image distribution included the torso, legs, and hands.ConclusionsThe accuracy of this system is comparable to the best to‐date published algorithms for image classification challenges, suggesting this algorithm could boost diagnosis, therapy, and research of dermatological conditions.

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Section: Discussionmentioning

confidence: 99%

Automatic body part identification in real‐world clinical dermatological images using machine learning

Sitaru,

Oueslati,

Schielein

et al. 2023

J Deutsche Derma Gesell

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“…An ongoing study in Australia is examining the use of 3D-TBP in an integrated telemedicine network of 15 systems in urban, regional, and remote areas [34]. Among patients with melanoma, outcomes have been found to be poorer in rural regions than in urban regions [16,[35][36][37].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, 3D-TBP fulfills the prerequisites for teleconsultation. Teledermatology with 3D-TBP could also serve as a triage tool for appointments at the hospital [34]. Certain areas of the body (e.g., soles of feet, haired head, retroauricular area, the genital area, the mucous area, and other body folds) cannot be viewed on 3D-TBP [15] and must be examined separately by a physician.…”

Section: Discussionmentioning

confidence: 99%

Clinician’s Ability to Identify Non-Melanoma Skin Cancer on 3D-Total Body Photography Sectors that Were Initially Identified during In-Person Skin Examination with Dermoscopy

Hobelsberger,

Steininger,

Laske

et al. 2023

Dermatology

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Introduction: Non-melanoma skin cancer (NMSC) is a cause of significant morbidity and mortality in high-risk individuals. Total body photography (TBP) is currently used to monitor melanocytic lesions in patients with high risk for melanoma. The authors examined if three-dimensional (3D)-TBP could be useful for diagnosis of NMSC. Methods: Patients (n = 129; 52 female, 77 male) with lesions suspicious for NMSC who had not yet had a biopsy underwent clinical examination followed by examination of each lesion with 3D-TBP Vectra®WB360 (Canfield Scientific, Parsippany, NJ, USA) and dermoscopy. Results: The 129 patients had a total of 182 lesions. Histological examination was performed for 158 lesions; the diagnoses included basal cell carcinoma (BCC; n = 107), squamous cell carcinoma (SCC; n = 27), in-situ SCC (n = 15). Lesions were located in the head/neck region (n = 138), trunk (n = 21), and limbs (n = 23). Of the 182 lesions examined, 12 were not visible on 3D-TBP; reasons for not being visible included location under hair and on septal of nose. Two lesions appeared only as erythema in 3D-TBP but were clearly identifiable on conventional photographs. Sensitivity of 3D-TBP was lower than that of dermoscopy for BCC (73% vs. 79%, p = 0.327), higher for SCC (81% vs. 74%, p = 0.727), and lower for in-situ SCC (0% vs. 33%, p = 125). Specificity of 3D-TBP was lower than that of dermoscopy for BCC (77% vs. 82%, 0.581), lower for SCC (75% vs. 84%, p = 0.063), and higher for in-situ SCC (97% vs. 94%, p = 0.344). Diagnostic accuracy of 3D-TBP was lower than that of dermoscopy for BCC (75% vs. 80%), lower for SCC (76% vs. 82%), and lower for in-situ SCC (88% vs. 89%). Lesion location was not associated with diagnostic confidence in dermoscopy (p = 0.152) or 3D-TBP (p = 0.353). If only lesions with high confidence were included in the calculation, diagnostic accuracy increased for BCC (n = 27; sensitivity 85%, specificity 85%, diagnostic accuracy 85%), SCC (n = 10; sensitivity 90%, specificity 80%, diagnostic accuracy 83%), and for in-situ SCC (n = 2; sensitivity 0%, specificity 100%, diagnostic accuracy 95%). Conclusion: Diagnostic accuracy appears to be slightly lower for 3D-TBP in comparison to dermoscopy. However, there is no statistically significant difference in the sensitivity and specificity of 3D-TBP and dermoscopy for NMSC. Diagnostic accuracy increases, if only lesions with high confidence are included in the calculation. Further studies are necessary to determine if 3D-TBP can improve management of NMSC.

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“…We conducted a cross‐sectional survey of the general public to explore the barriers and facilitators of 3D total‐body photography. This study formed part of the Australian Centre Research Foundation's Australian Centre of Excellence in Melanoma Imaging and Diagnosis (ACRF ACEMID) research program, which aims to reduce the burden of melanoma 7 . Given the geographical barriers to healthcare access and the potential for telehealth capabilities to address this medical divide, we aimed to collect data from individuals living in metropolitan, regional, rural, and remote areas of Australia to allow for comparisons across geographical locations.…”

Section: Introductionmentioning

confidence: 99%

Consumer views of melanoma early detection using 3D total‐body photography: cross‐sectional survey

et al. 2023

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Background Three-dimensional (3D) total-body photography is a recent advance in melanoma early detection that can aid in monitoring and identifying new and changing skin lesions over time.Methods A cross-sectional survey of adults living in metropolitan and rural areas of Australia was conducted to assess perceptions of 3D total-body photography. Participants completed a survey detailing their previous use of skin cancer photography, personal skin checking history, perceptions of 3D total-body photography, and willingness to pay.Participants were also asked to describe the potential barriers and facilitators of 3D totalbody photography in their own words. Results A total of 1056 participants completed the survey, with 739 (70%) from metropolitan areas of Australia and 317 (30%) from rural areas. Most participants (95%, n = 1004/1056) indicated they would consider using 3D total-body photography if it became commercially available at their regular medical practice. Most participants indicated 3D total-body photography would be effective to identify suspicious skin spots (94%, n = 995/ 1056), monitor lesion changes (94%, n = 997/1056), and reduce skin cancer related anxiety (90%, n = 950/1055). In open-ended feedback, participants (87%, n = 918/1056) identified perceived benefits, including more comprehensive screenings, earlier detection, and less human error. Participants (84%, n = 889/1056) also identified potential barriers to 3D total-body photography, including cost, accessibility and availability, trust in the technology, and digital security concerns.Conclusions Participant feedback indicated a high level of acceptance and confidence in the technology. To facilitate clinical translation, addressing consumer-identified barriers to 3D total-body photography will be vital.

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A 3D total-body photography research network: the Australian experiment

Cited by 16 publications

References 13 publications

Automatic body part identification in real‐world clinical dermatological images using machine learning

Automatic body part identification in real‐world clinical dermatological images using machine learning

Clinician’s Ability to Identify Non-Melanoma Skin Cancer on 3D-Total Body Photography Sectors that Were Initially Identified during In-Person Skin Examination with Dermoscopy

Consumer views of melanoma early detection using 3D total‐body photography: cross‐sectional survey

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