Medical AI Research Center, Samsung Medical Center, Seoul, Republic of Korea.
Department of Otorhinolaryngology-Head and Neck Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
PLoS One. 2022 Feb 25;17(2):e0263125. doi: 10.1371/journal.pone.0263125. eCollection 2022.
This study aims to develop artificial intelligence (AI) system to automatically classify patients with maxillary sinus fungal ball (MFB), chronic rhinosinusitis (CRS), and healthy controls (HCs).
We collected 512 coronal image sets from ostiomeatal unit computed tomography (OMU CT) performed on subjects who visited a single tertiary hospital. These data included 254 MFB, 128 CRS, and 130 HC subjects and were used for training the proposed AI system. The AI system takes these 1024 sets of half CT images as input and classifies these as MFB, CRS, or HC. To optimize the classification performance, we adopted a 3-D convolutional neural network of ResNet 18. We also collected 64 coronal OMU CT image sets for external validation, including 26 MFB, 18 CRS, and 20 HCs from subjects from another referral hospital. Finally, the performance of the developed AI system was compared with that of the otolaryngology resident physicians.
Classification performance was evaluated using internal 5-fold cross-validation (818 training and 206 internal validation data) and external validation (128 data). The area under the receiver operating characteristic over the internal 5-fold cross-validation and the external validation was 0.96 ±0.006 and 0.97 ±0.006, respectively. The accuracy of the internal 5-fold cross-validation and the external validation was 87.5 ±2.3% and 88.4 ±3.1%, respectively. As a result of performing a classification test on external validation data from six otolaryngology resident physicians, the accuracy was obtained as 84.6 ±11.3%.
This AI system is the first study to classify MFB, CRS, and HC using deep neural networks to the best of our knowledge. The proposed system is fully automatic but performs similarly to or better than otolaryngology resident physicians. Therefore, we believe that in regions where otolaryngology specialists are scarce, the proposed AI will perform sufficiently effective diagnosis on behalf of doctors.
本研究旨在开发人工智能（AI）系统，以自动对上颌窦真菌球（MFB）、慢性鼻-鼻窦炎（CRS）和健康对照组（HC）患者进行分类。
我们从单一三级医院就诊的患者中收集了 512 套额窦口-鼻道复合体计算机断层扫描（OMU CT）的冠状图像。这些数据包括 254 例 MFB、128 例 CRS 和 130 例 HC 患者，用于训练所提出的 AI 系统。该 AI 系统以这 1024 套半 CT 图像作为输入，并将其分类为 MFB、CRS 或 HC。为了优化分类性能，我们采用了 ResNet 18 的 3-D 卷积神经网络。我们还从另一家转诊医院的患者中收集了 64 套冠状 OMU CT 图像，用于外部验证，包括 26 例 MFB、18 例 CRS 和 20 例 HC。最后，比较了所开发的 AI 系统与耳鼻喉科住院医师的表现。
使用内部 5 折交叉验证（818 次训练和 206 次内部验证数据）和外部验证（128 次数据）评估分类性能。内部 5 折交叉验证和外部验证的受试者工作特征曲线下面积分别为 0.96±0.006 和 0.97±0.006。内部 5 折交叉验证和外部验证的准确率分别为 87.5±2.3%和 88.4±3.1%。对来自六名耳鼻喉科住院医师的外部验证数据进行分类测试的结果为 84.6±11.3%。
据我们所知，这是第一个使用深度神经网络对 MFB、CRS 和 HC 进行分类的 AI 系统。该系统完全自动化，但表现与耳鼻喉科住院医师相似或优于住院医师。因此，我们认为在耳鼻喉科专家稀缺的地区，所提出的 AI 系统可以代表医生进行足够有效的诊断。
