Aquatic Based Research, Southern Cross University, Bilinga, Queensland, Australia.
Faculty of Health, Southern Cross University, Bilinga, Queensland, Australia.
PeerJ. 2023 Aug 8;11:e15737. doi: 10.7717/peerj.15737. eCollection 2023.
There is enthusiasm for implementing artificial intelligence (AI) to assist clinicians detect skin cancer. Performance metrics of AI from dermoscopic images have been promising, with studies documenting sensitivity and specificity values equal to or superior to specialists for the detection of malignant melanomas (MM). Early detection rates would particularly benefit Australia, which has the worlds highest incidence of MM . The detection of skin cancer may be delayed due to late screening or the inherent difficulty in diagnosing early skin cancers which often have a paucity of clinical features and may blend into sun damaged skin. Individuals who participate in outdoor sports and recreation experience high levels of intermittent ultraviolet radiation (UVR), which is associated with the development of skin cancer, including MM. This research aimed to assess the prevalence of skin cancer in individuals who regularly participate in activities outdoors and to report the performance parameters of a commercially available AI-powered software to assess the predictive risk of MM development.
Cross-sectional study design incorporating a survey, total body skin cancer screening and AI-embedded software capable of predictive scoring of queried MM.
A total of 423 participants consisting of surfers ( = 108), swimmers ( = 60) and walkers/runners ( = 255) participated. Point prevalence for MM was highest for surfers (6.48%), followed by walkers/runners (4.3%) and swimmers (3.33%) respectively. When compared to the general Australian population, surfers had the highest odds ratio (OR) for MM (OR 119.8), followed by walkers/runners (OR 79.74), and swimmers (OR 61.61) rounded out the populations. Surfers and swimmers reported comparatively lower lifetime hours of sun exposure (5,594 and 5,686, respectively) but more significant amounts of activity within peak ultraviolet index compared with walkers/runners (9,554 h). A total of 48 suspicious pigmented lesions made up of histopathology-confirmed MM ( = 15) and benign lesions ( = 33) were identified. The performance of the AI from this clinical population was found to have a sensitivity of 53.33%, specificity of 54.44% and accuracy of 54.17%.
Rates of both keratinocyte carcinomas and MM were notably higher in aquatic and land-based enthusiasts compared to the general Australian population. These findings further highlight the clinical importance of sun-safe protection measures and regular skin screening in individuals who spend significant time outdoors. The use of AI in the early identification of MM is promising. However, the lower-than-expected performance metrics of the AI software used in this study indicated reservations should be held before recommending this particular version of this AI software as a reliable adjunct for clinicians in skin imaging diagnostics in patients with potentially sun damaged skin.
人们热衷于利用人工智能（AI）来辅助临床医生检测皮肤癌。基于皮肤镜图像的 AI 性能指标已经很有前景，研究记录了 AI 检测恶性黑色素瘤（MM）的敏感性和特异性值与专家相当或优于专家。早期检测率将特别有益于澳大利亚，因为澳大利亚的 MM 发病率是世界上最高的。由于晚期筛查或早期皮肤癌诊断的固有困难，皮肤癌的检测可能会被延误，因为早期皮肤癌通常缺乏临床特征，并且可能与晒伤的皮肤混合在一起。经常参加户外运动和娱乐活动的人会受到间歇性紫外线辐射（UVR）的高水平照射，这与皮肤癌的发展有关，包括 MM。本研究旨在评估定期参加户外活动的个体中皮肤癌的患病率，并报告一种商用 AI 驱动软件评估 MM 发展预测风险的性能参数。
采用横断面研究设计，包括问卷调查、全身皮肤癌筛查和能够对查询 MM 进行预测评分的 AI 嵌入式软件。
共有 423 名参与者参加了研究，其中冲浪者（n=108）、游泳者（n=60）和步行/跑步者（n=255）。MM 的点患病率以冲浪者最高（6.48%），其次是步行/跑步者（4.3%）和游泳者（3.33%）。与一般澳大利亚人口相比，冲浪者患 MM 的可能性最高（OR 119.8），其次是步行/跑步者（OR 79.74），游泳者（OR 61.61）排在第三位。冲浪者和游泳者报告的终生日照时间相对较少（分别为 5594 和 5686 小时），但与步行/跑步者相比，他们在紫外线指数高峰期的活动量更大（9554 小时）。共发现 48 个可疑色素性病变，其中包括组织病理学证实的 MM（n=15）和良性病变（n=33）。从该临床人群中获得的 AI 性能被发现具有 53.33%的敏感性、54.44%的特异性和 54.17%的准确性。
与一般澳大利亚人口相比，水生和陆地爱好者的角化细胞癌和 MM 发病率明显更高。这些发现进一步强调了在大量时间暴露于户外的人群中，采取防晒保护措施和定期皮肤筛查的重要性。AI 在早期识别 MM 中的应用具有前景。然而，本研究中使用的 AI 软件的性能指标低于预期，这表明在推荐该特定版本的 AI 软件作为临床医生在可能有晒伤皮肤的患者的皮肤成像诊断中的可靠辅助工具之前，应持保留态度。
