Department of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden.
Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
Acta Obstet Gynecol Scand. 2024 Oct;103(10):2053-2060. doi: 10.1111/aogs.14906. Epub 2024 Jul 31.
Our objective was to determine whether the educational game SonoQz can improve diagnostic performance in ultrasound assessment of ovarian tumors.
The SonoQz mobile application was developed as an educational tool for medical doctors to practice ultrasound assessment, based on still images of ovarian tumors. The game comprises images from 324 ovarian tumors, examined by an ultrasound expert prior to surgery. A training phase, where the participants assessed at least 200 cases in the SonoQz app, was preceded by a pretraining test, and followed by a posttraining test. Two equal tests (A and B), each consisting of 20 cases, were used as pre- and posttraining tests. Half the users took test A first, B second, and the remaining took the tests in the opposite order. Users were asked to classify the tumors (1) according to International Ovarian Tumor Analysis (IOTA) Simple Rules, (2) as benign or malignant, and (3) suggest a specific histological diagnosis. Logistic mixed models with fixed effects for pre- and posttraining tests, and crossed random effects for participants and cases, were used to determine any improvement in test scores, sensitivity, and specificity.
Fifty-eight doctors from 19 medical centers participated. Comparing the pre- and posttraining test, the median of correctly classified cases, in Simple Rules assessment increased from 72% to 83%, p < 0.001; in classifying the lesion as benign or malignant tumors from 86% to 95%, p < 0.001; and in making a specific diagnosis from 43% to 63%, p < 0.001. When classifying tumors as benign or malignant, at an unchanged level of sensitivity (98% vs. 97%, p = 0.157), the specificity increased from 70% to 89%, p < 0.001.
Our results indicate that the educational game SonoQz is an effective tool that may improve diagnostic performance in assessing ovarian tumors, specifically by reducing the number of false positives while maintaining high sensitivity.
我们的目的是确定教育游戏 SonoQz 是否可以提高卵巢肿瘤超声评估的诊断性能。
SonoQz 移动应用程序是作为一种医学医生练习超声评估的教育工具而开发的，其基础是卵巢肿瘤的静态图像。该游戏包含了 324 个卵巢肿瘤的图像，这些图像在手术前由一位超声专家进行了检查。在进行培训阶段之前，参与者首先进行了预培训测试，然后进行了培训后测试。培训阶段包括在 SonoQz 应用程序中评估至少 200 个病例。两个相等的测试（A 和 B），每个测试包含 20 个病例，用作预测试和后测试。一半的用户首先进行测试 A，然后进行测试 B，其余的用户则相反。要求用户根据国际卵巢肿瘤分析（IOTA）简单规则对肿瘤进行分类（1），将肿瘤分为良性或恶性（2），并提出特定的组织学诊断（3）。使用具有固定预测试和后测试效果的逻辑混合模型，并具有参与者和病例的交叉随机效果，以确定测试分数、敏感性和特异性是否有所提高。
来自 19 个医疗中心的 58 名医生参与了此次研究。与预测试相比，在简单规则评估中，正确分类的病例中位数从 72%增加到 83%，p<0.001；将病变分类为良性或恶性肿瘤的比例从 86%增加到 95%，p<0.001；以及进行特定诊断的比例从 43%增加到 63%，p<0.001。当将肿瘤分类为良性或恶性时，在保持高敏感性（98%对 97%，p=0.157）不变的情况下，特异性从 70%增加到 89%，p<0.001。
我们的结果表明，教育游戏 SonoQz 是一种有效的工具，可以提高评估卵巢肿瘤的诊断性能，特别是通过减少假阳性数量，同时保持高敏感性。
