术中光学成像技术的兴起为临床手术提供了更加便捷和直观的观察手段。传统的术中光学成像方法包括开放式光学成像和术中腔镜、内镜成像等，这些方法保障了临床手术的顺利进行，同时也促进了微创手术的发展。随后发展起来的术中光学成像技术还有窄带腔镜成像、术中激光共聚焦显微成像和近红外激发荧光成像等。术中光学成像技术可以辅助医生精准定位肿瘤、快速区分良恶性组织和检测微小病灶等，在诸多临床应用领域表现出了较好的应用效果。但术中光学成像技术也存在成像质量受限、缺乏有力的成像分析工具，以及只能成像表浅组织的问题。机器学习的加入，有望突破瓶颈，进一步推动术中光学成像技术的发展。本文针对术中光学成像技术，对机器学习在这一领域的应用研究展开调研，具体包括：机器学习对术中光学成像质量的优化、辅助术中光学成像的智能分析，以及辅助基于术中光学影像的3维建模等内容。本文对机器学习在术中光学成像领域的应用进行总结和分析，特别叙述了深度学习方法在该领域的应用前景，为后续的研究提供更宽泛的思路。

The rise of intraoperative optical imaging technologies provides a convenient and intuitive observation method for clinical surgery. Traditional intraoperative optical imaging methods include open optical and intraoperative endoscopic imaging. These methods ensure the smooth implementation of clinical surgery and promote the development of minimally invasive surgery. Subsequent methods include narrow-band endoscopic, intraoperative laser confocal microscopy, and near-infrared excited fluorescence imaging. Narrow-band endoscopic imaging uses a filter to filter out the broad-band spectrum emitted by the endoscope light source, leaving only the narrow-band spectrum for the diagnosis of various diseases of the digestive tract. The narrow-band spectrum is conducive to enhancing the image of the gastrointestinal mucosa vessels. In some lesions with microvascular changes, the narrow-band imaging system has evident advantages over ordinary endoscopy in distinguishing lesions. Narrow-band endoscopic imaging has also been widely used in the fields of otolaryngology, respiratory tract, gynecological endoscopy, and laparoscopic surgery in addition to the digestive tract. Intraoperative laser confocal microscopy is a new type of imaging method. It can realize superficial tissue imaging in vivo and provide pathological information by using the principle of excited fluorescence imaging. This imaging method has high clarity due to the application of confocal imaging and can be used for lesion positioning. Near-infrared excited fluorescence imaging uses excitation fluorescence imaging equipment combined with corresponding fluorescent contrast agents (such as ICG(indocyanine green), and methylene blue) to achieve intraoperative specific imaging of lesions, tissues, and organs in vivo. The basic principle is to stimulate the contrast agent accumulated in the tissue, the fluorescent contrast agent emits a fluorescent signal, and real-time imaging is realized by collecting the signals. In clinical research, the near-infrared fluorescence imaging technology is often used for lymphatic vessel tracing and accurate tumor resection. Contrast agents have different imaging spectral bands; hence, the corresponding near-infrared fluorescence imaging equipment is also developing to a multichannel imaging mode to image substantial contrast agents and label multiple tissues in the same field of view specifically during surgery. Multichannel near-infrared fluorescent surgical navigation equipment that has been gradually developed can realize simultaneous fluorescence imaging of multiple organs and tissues. These intraoperative optical imaging technologies can assist doctors in accurately locating tumors, rapidly distinguishing between benign and malignant tissues, and detecting small lesions. They have gained benefits in many clinical applications. However, optical imaging is susceptible to interference from ambient light, and optical signals are difficult to propagate in tissues without optical signal absorption and scattering. Intraoperative optical imaging technologies have the problems of limited imaging quality and superficial tissue imaging. In clinical research, intelligent analysis of preoperative imaging is fiercely developing, while information analysis of intraoperative imaging is still lacking of powerful analytical tools and analytical methods. The study of effective intraoperative optical imaging analysis algorithms needs further exploration. Machine learning is a tool developed with the age of computer information technology and is expected to provide an effective solution to the abovementioned problems. With the accumulation and explosion of data volume, deep learning, as a type of machine learning, is an end-to-end algorithm. It can gain the internal relationship among things autonomously through network training, establish an empirical model, and realize the function of traditional algorithms. Deep learning has shown enhanced results in the analysis and processing of natural images and is being continuously promoted and applied to various fields. Machine learning provides powerful technical means for intelligent analysis, image processing, and three-dimensional reconstruction, but the application research of using machine learning in intraoperative optical imaging is relatively few. The addition of machine learning is expected to break through the bottleneck and promote the development of intraoperative optical imaging technologies. This article focuses on intraoperative optical imaging technologies and investigates the application of machine learning in this field in recent years, including optimizing intraoperative optical imaging quality, assisting intelligent analysis of intraoperative optical imaging, and promoting three-dimensional modeling of intraoperative optical imaging. In the field of machine learning for intraoperative optical imaging optimization, existing research includes target detection of specific tissues, such as soft tissue segmentation and image fusion, and optimization of imaging effects, such as resolution enhancement of near-infrared fluorescence imaging during surgery and intraoperative endoscopic smoke removal. Furthermore, machine learning assists doctors in performing intraoperative optical imaging analysis, including the identification of benign and malignant tissues and the classification of lesion types and grades. Therefore, it can provide a timely reference value for the surgeon to judge the state of the patient during the clinical operation and before the pathological examination. In the field of intraoperative optical imaging reconstruction, machine learning can be combined with preoperative images (such as computed tomography and magnetic resonance imaging) to assist in intraoperative soft tissue reconstruction, or it can be based on intraoperative images for three-dimensional reconstruction. It can be used for localization, three-dimensional organ morphology reconstruction, and tracking of intraoperative tissues and surgical instruments. Thus, machine learning is expected to provide corresponding technical foundation for robotic surgery and augmented reality surgery in the future.This article summarizes and analyzes the application of machine learning in the field of intraoperative optical imaging and describes the application prospects of deep learning. As a review, it investigates the application research of machine learning in intraoperative optical imaging mainly from three aspects: intraoperative optical image optimization, intelligent analysis of optical imaging, and three-dimensional reconstruction. We also introduce related research and expected effects in the above fields. At the end of this article, the application of machine learning in the field of intraoperative optical imaging technologies is discussed, and the advantages and possible problems of machine-learning methods are analyzed. Furthermore, this article elaborates the possible future development direction of intraoperative optical imaging combined with machine learning, providing a broad view for subsequent research.