3D Whole-body skin imaging for automated melanoma detection.

BACKGROUND: Existing artificial intelligence for melanoma detection has relied on analysing images of lesions of clinical interest, which may lead to missed melanomas. Tools analysing the entire skin surface are lacking. OBJECTIVES: To determine if melanoma can be distinguished from other skin lesions using data from automated analysis of 3D-images. METHODS: Single-centre, retrospective, observational convenience sample of patients diagnosed with melanoma at a tertiary care cancer hospital. Eligible participants were those with a whole-body 3D-image captured within 90 days prior to the diagnostic skin biopsy. 3D-images were obtained as standard of care using VECTRA WB360 Whole Body 3-dimensional Imaging System (Canfield Scientific). Automated data from image processing (i.e. lesion size, colour, border) for all eligible participants were exported from VECTRA DermaGraphix research software for analysis. The main outcome was the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 35 patients contributed 23,538 automatically identified skin lesions >2 mm in largest diameter (102-3021 lesions per participant). All were White patients and 23 (66%) were males. The median (range) age was 64 years (26-89). There were 49 lesions of melanoma and 22,489 lesions that were not melanoma. The AUC for the prediction model was 0.94 (95% CI: 0.92-0.96). Considering all lesions in a patient-level analysis, 14 (28%) melanoma lesions had the highest predicted score or were in the 99th percentile among all lesions for an individual patient. CONCLUSIONS: In this proof-of-concept pilot study, we demonstrated that automated analysis of whole-body 3D-images using simple image processing techniques can discriminate melanoma from other skin lesions with high accuracy. Further studies with larger, higher quality, and more representative 3D-imaging datasets would be needed to improve and validate these results.

Changes in shear wave speed pre- and post-induction of labor: a feasibility study.

OBJECTIVE: To explore the feasibility of using shear wave speed (SWS) estimates to detect differences in cervical softening pre- and post-ripening in women undergoing induction of labor. METHODS: Subjects at 37-41 weeks' gestation undergoing cervical ripening before induction of labor were recruited (n = 20). Examinations, performed prior to administration of misoprostol and 4 h later included Bishop score, transvaginal ultrasound measurement of cervical length, and 10 replicate SWS measurements using an ultrasound system equipped with a prototype transducer (128 element, 3 mm diameter, 14 mm aperture) attached to the clinician's hand. Subjects were divided into two groups, 'not-in-labor' and 'marked-progression', based on cervical evaluation at the second examination. Measurements were compared via individual paired hypotheses tests and using a linear mixed model, with the latter also used to compare groups. Spearman's rank correlation coefficient was used to compare SWS with Bishop score. The linear mixed model can take into account clustered data and accommodate multiple predictors simultaneously. RESULTS: The Wilcoxon signed-rank paired test established a significant difference in pre- and post-ripening SWS, with mean SWS estimates of 2.53 ± 0.75 and 1.54 ± 0.31 m/s, respectively (P < 0.001) in the not-in-labor group (decrease in stiffness) and 1.58 ± 0.33 and 2.35 ± 0.65 m/s for the marked-progression group (increase in stiffness). The linear mixed model corroborated significant differences in pre- and post-ripening measurements in individual subjects (P < 0.001) as well as between groups (P < 0.0001). SWS estimates were significantly correlated with digitally-assessed cervical softness and marginally correlated with Bishop score as assessed by Spearman's rank correlation coefficient. CONCLUSIONS: In-vivo SWS estimates detected stiffness differences before and after misoprostol-induced softening in term pregnancies. This ultrasonic shear elasticity imaging technique shows promise for assessing cervical softness.