Sabazade Shiva, Lumia Michalski Marco A, Wittskog Sara, Holmström Mats, Nilsson Maria, Stålhammar Gustav
Ocular Oncology Service, St. Erik Eye Hospital, Stockholm, Sweden.
Department of Clinical Neuroscience, Division of Eye and Vision, Karolinska Institutet, Stockholm, Sweden.
Transl Vis Sci Technol. 2025 Jun 2;14(6):29. doi: 10.1167/tvst.14.6.29.
To evaluate the diagnostic accuracy of Swedish opticians/optometrists when triaging small pigmented choroidal lesions and determine whether the MelAInoma deep learning algorithm improves referral decisions.
Twenty-nine opticians/optometrists graded 25 fundus photographs (5 melanomas, 20 nevi) with the Mushroom shape, Orange pigment, Large size, Enlargement, Subretinal fluid (MOLES) system and recorded referrals; 25 then used MelAInoma. Predefined referral thresholds were MOLES ≥1 or ≥3 and MelAInoma >63. Diagnostic test statistics-including sensitivity, specificity, positive (PPV) and negative (NPV) predictive values, accuracy, and standard deviation (SD)-plus odds of correct referral and decision curve net benefit were computed.
With MOLES ≥1, mean ± SD sensitivity was 98% ± 6%, specificity was 17% ± 16%, PPV was 23%, NPV was 99%, and accuracy was 33%. Raising the cutoff to MOLES ≥3 lowered sensitivity to 75% ± 29% but increased specificity to 53% ± 28%, PPV to 34%, NPV to 93%, and accuracy to 57%. MelAInoma achieved 80% sensitivity, 90% specificity, PPV 67%, NPV 95%, and 88% accuracy. Algorithm guidance quadrupled the odds of correctly referring a melanoma, reduced false-positive referrals 10-fold, and provided net clinical benefit to unaided triage across all relevant threshold probabilities.
Although the findings are based on a small image set, including only five melanomas, which limits generalizability, the results suggest that opticians and optometrists detect small choroidal melanomas with high sensitivity but limited specificity. Incorporating MelAInoma at the predefined threshold reduced sensitivity, substantially improved specificity, and markedly reduced unnecessary referrals.
MelAInoma offers practical artificial intelligence support that can streamline community referrals and facilitate earlier treatment of uveal melanoma.
评估瑞典验光师/视光师在对小的色素性脉络膜病变进行分诊时的诊断准确性,并确定黑色素瘤深度学习算法是否能改善转诊决策。
29名验光师/视光师使用蘑菇形状、橙色色素、大尺寸、扩大、视网膜下液(MOLES)系统对25张眼底照片(5例黑色素瘤、20例痣)进行分级,并记录转诊情况;其中25人随后使用黑色素瘤算法。预定义的转诊阈值为MOLES≥1或≥3以及黑色素瘤算法评分>63。计算诊断测试统计数据,包括敏感性、特异性、阳性(PPV)和阴性(NPV)预测值、准确性和标准差(SD),以及正确转诊的几率和决策曲线净效益。
当MOLES≥1时,平均±标准差敏感性为98%±6%,特异性为17%±16%,PPV为23%,NPV为99%,准确性为33%。将阈值提高到MOLES≥3会使敏感性降至75%±29%,但特异性提高到53%±28%,PPV提高到34%,NPV提高到93%,准确性提高到57%。黑色素瘤算法的敏感性为80%,特异性为90%,PPV为67%,NPV为95%,准确性为88%。算法指导使正确转诊黑色素瘤的几率提高了四倍,将假阳性转诊减少了10倍,并在所有相关阈值概率下为无辅助分诊提供了临床净效益。
尽管研究结果基于一个小的图像集,仅包括5例黑色素瘤,这限制了其普遍性,但结果表明验光师和视光师检测小的脉络膜黑色素瘤的敏感性高但特异性有限。在预定义阈值下纳入黑色素瘤算法会降低敏感性,但显著提高特异性,并明显减少不必要的转诊。
黑色素瘤算法提供了实用的人工智能支持,可以简化社区转诊并促进葡萄膜黑色素瘤的早期治疗。