Adherence to melanoma screening and surveillance skin check schedules tailored to personal risk.

Population-wide skin cancer screening is not currently recommended in most countries. Instead, most clinical guidelines incorporate risk-based recommendations for skin checks, despite limited evidence around implementation and adherence to recommendations in practice. We aimed to determine adherence to personal risk-tailored melanoma skin check schedules and explore reasons influencing adherence. Patients (with/without a previous melanoma) attending tertiary dermatology clinics at the Melanoma Institute Australia, Sydney, Australia, were invited to complete a melanoma risk assessment questionnaire via iPad and provided with personal risk information alongside a risk-tailored skin check schedule. Data were collected from the risk tool, clinician-recorded data on schedule deviations, and appointment booking system. Post-consultation, we conducted semi-structured interviews with patients and clinic staff. We used a convergent segregated mixed methods approach for analysis. Interviews were audio recorded, transcribed and data were analysed thematically. Participant data were analysed from clinic records (n = 247) and interviews (n = 29 patients, 11 staff). Overall, there was 62% adherence to risk-tailored skin check schedules. In cases of non-adherence, skin checks tended to occur more frequently than recommended. Decisions to deviate were similarly influenced by patients (44%) and clinicians (56%). Themes driving non-adherence among patients included anxiety and wanting autonomy around decision-making, and among clinicians included concerns around specific lesions and risk estimate accuracy. There was moderate adherence to a clinical service program of personal risk-tailored skin check recommendations. Further adherence may be gained by incorporating strategies to identify and assist patients with high levels of anxiety and supporting clinicians to communicate risk-based recommendations with patients.

The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection.

Patient demographic characteristics and risk factors associated with sun protection behaviours in specialist melanoma clinics.

OBJECTIVE: We investigated the association between sun protection behaviours and demographic and melanoma risk characteristics of patients attending Australian melanoma specialist clinics. This may assist in targeting and tailoring melanoma prevention patient education for people at high-risk and specific population subgroups. METHODS: A cross-sectional analysis of questionnaire data collected from participants attending the dermatology clinics at two major melanoma centres in Sydney, Australia between February 2021 and September 2023. The primary outcome was Sun Protection Habits (SPH) index (a summary score measured as habitual past month use of sunscreen, hats, sunglasses, a shirt with sleeves that covers the shoulders, limiting midday sun exposure and seeking shade, using a Likert scale). The primary analysis considered the SPH index and its component items scored as continuous. RESULTS: Data from 883 people were analysed. Factors associated with less frequent sun protection behaviours overall included male gender, no personal history of melanoma, lower perceived risk, lower calculated 10-year risk of developing melanoma, and no private health insurance. People aged >61 years reported lower use of sunscreen but higher use of hats and sleeved-shirts compared with people in the younger age group. There was no difference in overall sun protection behaviours according to family history of melanoma, country of birth or by lifetime melanoma risk among people without a personal history of melanoma. CONCLUSIONS: These findings highlight the potential for targeting high-risk individuals with less frequent use of sun protection for patient education, public health messaging and ultimately improving sun protection behaviours.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately. However, guidelines for labelling AI-based SaMD in dermatology are lacking, which may result in products failing to provide essential information about algorithm development and performance metrics. This review examines existing labelling guidelines for AI-based SaMD across visual medical specialties, with a specific focus on dermatology. Common recommendations for labelling are identified and applied to currently available dermatology AI-based SaMD mobile applications to determine usage of these labels. Of the 21 AI-based SaMD mobile applications identified, none fully comply with common labelling recommendations. Results highlight the need for standardized labelling guidelines. Ensuring transparency and accessibility of information is essential for the safe integration of AI into health care and preventing potential risks associated with inaccurate clinical decisions.

Skin cancer referrals by nonmedical practitioners: a prospective observational study.

The aim of this study was to investigate the appropriateness of suspected skin cancer referrals made by nonmedical practitioners (NMPs) and compare this with referrals made by local general practitioners (GPs). Data were collected prospectively from patients referred from primary care to a UK hospital dermatology department. The profession of the referrer was ascertained from review of referral letters and direct questioning. Patient records and subsequent histology reports were reviewed to determine the ultimate diagnoses. Eighty-nine per cent of patients (n = 668/753) were referred by GPs vs. 11.3% (n = 85/753) by NMPs. Fifty-one per cent of patients (n = 340/668) in the GP group and 55% (n = 47/85) in the NMP group were discharged without intervention (P = 0.45). An ultimate diagnosis of skin malignancy was made in 196 of 668 (29.3%) patients in the GP and 25 of 85 (29%) patients in the NMP group (P = 0.99). These early data suggest significant potential for NMPs to become more involved in skin lesion assessment.