Application of artificial intelligence in cancer diagnosis and tumor nanomedicine.

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging.

In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body's central region and its planar normal vectors to achieve automatic adjustment of the camera's positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart's structure and function. A series of experimental validations on human and cardiac models have assessed the system's effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.

An ingestible near-infrared fluorescence capsule endoscopy for specific gastrointestinal diagnoses.

Early diagnosis of gastrointestinal (GI) diseases is important to effectively prevent carcinogenesis. Capsule endoscopy (CE) can address the pain caused by wired endoscopy in GI diagnosis. However, existing CE approaches have difficulty effectively diagnosing lesions that do not exhibit obvious morphological changes. In addition, the current CE cannot achieve wireless energy supply and attitude control at the same time. Here, we successfully developed a novel near-infrared fluorescence capsule endoscopy (NIFCE) that can stimulate and capture near-infrared (NIR) fluorescence images to specifically identify subtle mucosal microlesions and submucosal lesions while capturing conventional white light (WL) images to detect lesions with significant morphological changes. Furthermore, we constructed the first synergetic system that simultaneously enables multi-attitude control in NIFCE and supplies long-term power, thus addressing the issue of excessive power consumption caused by the NIFCE emitting near-infrared light (NIRL). We performed in vivo experiments to verify that the NIFCE can specifically "light up" tumors while sparing normal tissues by synergizing with probes actively aggregated in tumors, thus realizing specific detection and penetration. The prototype NIFCE system represents a significant step forward in the field of CE and shows great potential in efficiently achieving early targeted diagnosis of various GI diseases.