骨骼肌是膳食葡萄糖的主要加工者，将其储存在无数的糖原颗粒中。它们的数量随细胞位置以及生理和病理生理状态而变化。开发人工智能模型是为了从人体肌肉的电子显微镜图像中推导出颗粒糖原含量。建立了两种UNet类型的语义分割模型：“位置”将像素分类为属于细胞中的不同区域； “颗粒”识别颗粒内的像素。根据他们的联合输出，计算出阳性 (MHS) 或阴性 (MHN) 患者图像的像素分数 pf，以进行恶性高热敏感性测试。 pf 用于推导 vf，即颗粒所占的体积分数。关系 vf (pf) 源自对包含实际浓度虚拟颗粒的体积（“篮子”）的模拟。模拟颗粒的直径与真实颗粒的直径相匹配，这是通过采用为钙火花设计的实用程序来测量的。将这种关系应用于图像中测量的 pf，计算出每个区域和患者的 vf，并从中计算出糖原浓度。肌原纤维间间隙和肌节 I 带的颗粒含量最高。测量的糖原浓度足够低，足以允许大量存在非颗粒糖原。 MHS 样品的浓度大约低三倍（在分级测试中显着），与 MHS 中葡萄糖加工减少的早期证据一致。 AI 模型和从二维图像推断三维大小的方法应该适用于来自患者和动物模型以及不同疾病状况的各种图像的其他任务。© 2024 Ríos 等人。

Skeletal muscle, the major processor of dietary glucose, stores it in myriad glycogen granules. Their numbers vary with cellular location and physiological and pathophysiological states. AI models were developed to derive granular glycogen content from electron-microscopic images of human muscle. Two UNet-type semantic segmentation models were built: "Locations" classified pixels as belonging to different regions in the cell; "Granules" identified pixels within granules. From their joint output, a pixel fraction pf was calculated for images from patients positive (MHS) or negative (MHN) to a test for malignant hyperthermia susceptibility. pf was used to derive vf, the volume fraction occupied by granules. The relationship vf (pf) was derived from a simulation of volumes ("baskets") containing virtual granules at realistic concentrations. The simulated granules had diameters matching the real ones, which were measured by adapting a utility devised for calcium sparks. Applying this relationship to the pf measured in images, vf was calculated for every region and patient, and from them a glycogen concentration. The intermyofibrillar spaces and the sarcomeric I band had the highest granular content. The measured glycogen concentration was low enough to allow for a substantial presence of non-granular glycogen. The MHS samples had an approximately threefold lower concentration (significant in a hierarchical test), consistent with earlier evidence of diminished glucose processing in MHS. The AI models and the approach to infer three-dimensional magnitudes from two-dimensional images should be adaptable to other tasks on a variety of images from patients and animal models and different disease conditions.© 2024 Ríos et al.