Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea. (Courtesy of Dr. Yang Jiao. See pages 488 ‒497 by Yang Jiao et al. for more information.)
Research into medical artificial intelligence (AI) has made significant advances in recent years, including surgical applications. This scoping review investigated AI-based decision support systems targeted at the intraoperative phase of surgery and found a wide range of technological approaches applied across several surgical specialties. Within the twenty-one (n=21) included papers, three main categories of motivations were identified for developing such technologies: (1) augmenting the information available to surgeons, (2) accelerating intraoperative pathology, and (3) recommending surgical steps. While many of the proposals hold promise for improving patient outcomes, important methodological shortcomings were observed in most of the reviewed papers that made it difficult to assess the clinical significance of the reported performance statistics. Despite limitations, the current state of this field suggests that a number of opportunities exist for future researchers and clinicians to work on AI for surgical decision support with exciting implications for improving surgical care.
Minimally invasive surgery, including laparoscopic and thoracoscopic procedures, benefits patients in terms of improved postoperative outcomes and short recovery time. The challenges in hand–eye coordination and manipulation dexterity during the aforementioned procedures have inspired an enormous wave of developments on surgical robotic systems to assist keyhole and endoscopic procedures in the past decades. This paper presents a systematic review of the state-of-the-art systems, picturing a detailed landscape of the system configurations, actuation schemes, and control approaches of the existing surgical robotic systems for keyhole and endoscopic procedures. The development challenges and future perspectives are discussed in depth to point out the need for new enabling technologies and inspire future researches.
A number of developed countries are rapidly turning into super-aged societies. Consequently, the demand for reduced surgical invasiveness and enhanced efficiency in the medical field has increased due to the need to reduce the physical burden on older patients and shorten their recovery period. Intelligent surgical robot systems offer high precision, high safety, and reduced invasiveness. This paper presents a review of current intelligent surgical robot systems. The history of robots and three types of intelligent surgical robots are discussed. The problems with current surgical robot systems are then analyzed. Several aspects that should be considered in designing new surgical systems are discussed in detail. The paper ends with a summary of the work and a discussion of future prospects for surgical robot development.
Artificial intelligence (AI) is gradually changing the practice of surgery with technological advancements in imaging, navigation, and robotic intervention. In this article, we review the recent successful and influential applications of AI in surgery from preoperative planning and intraoperative guidance to its integration into surgical robots. We conclude this review by summarizing the current state, emerging trends, and major challenges in the future development of AI in surgery.
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.
deep learning (DL) has achieved state-of-the-art performance in many digital pathology analysis tasks. Traditional methods usually require hand-crafted domain-specific features, and DL methods can learn representations without manually designed features. In terms of feature extraction, DL approaches are less labor intensive compared with conventional machine learning methods. In this paper, we comprehensively summarize recent DL-based image analysis studies in histopathology, including different tasks (e.g., classification, semantic segmentation, detection, and instance segmentation) and various applications (e.g., stain normalization, cell/gland/region structure analysis). DL methods can provide consistent and accurate outcomes. DL is a promising tool to assist pathologists in clinical diagnosis.
Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysfunction, and neuromuscular disorder, among other conditions. Identifying the causes of dyspnea in clinical practice, especially for the general practitioner, remains a challenge. This pilot study aimed to develop a computer-aided tool for improving the efficiency of differential diagnosis. The disease set with dyspnea as the chief complaint was established on the basis of clinical experience and epidemiological data. Differential diagnosis approaches were established and optimized by clinical experts. The artificial intelligence (AI) diagnosis model was constructed according to the dynamic uncertain causality graph knowledge-based editor. Twenty-eight diseases and syndromes were included in the disease set. The model contained 132 variables of symptoms, signs, and serological and imaging parameters. Medical records from the electronic hospital records of Suining Central Hospital were randomly selected. A total of 202 discharged patients with dyspnea as the chief complaint were included for verification, in which the diagnoses of 195 cases were coincident with the record certified as correct. The overall diagnostic accuracy rate of the model was 96.5%. In conclusion, the diagnostic accuracy of the AI model is promising and may compensate for the limitation of medical experience.
Disorders of sex development (DSD) are a group of rare complex clinical syndromes with multiple etiologies. Distinguishing the various causes of DSD is quite difficult in clinical practice, even for senior general physicians because of the similar and atypical clinical manifestations of these conditions. In addition, DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD. Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses. On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence. “Chaining” inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information. Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis. The model had an accuracy of 94.1%, which was significantly higher than that of interns and third-year residents. In conclusion, the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSD-related diseases.
The outbreak of coronavirus disease 2019 (COVID-19) has spread rapidly around the world. As of May 30, 2020, a total of 84 568 confirmed COVID-19 cases have been recorded in China, with a mortality rate of approximately 5.5%. Taizhou is a prefecture-level city in Zhejiang Province. A total of 146 cases were diagnosed in this epidemic, with a fatality rate of 0%. This condition is due to the establishment of an “Internet+” diagnosis and treatment model based on online medical application (APP), telemedicine, WeChat service, and consultation hotline in Taizhou. Taizhou led in opening the “COVID-19 Prevention and Treatment Special Line” in China, which is conducive to pre-hospital screening, suppressing social panic, and clinical support. Hospitals also carried out related online lectures and popularization of science. We summarize Taizhou’s COVID-19 prevention and control experience with telemedicine features, with a view to providing reference for the control of the epidemic at home and abroad.
Artificial intelligence (AI) is coming to medicine in a big wave. From making diagnosis in various medical conditions, following the latest advancements in scientific literature, suggesting appropriate therapies, to predicting prognosis and outcome of diseases and conditions, AI is offering unprecedented possibilities to improve care for patients. Gastroenterology is a field that AI can make a significant impact. This is partly because the diagnosis of gastrointestinal conditions relies a lot on image-based investigations and procedures (endoscopy and radiology). AI-assisted image analysis can make accurate assessment and provide more information than conventional analysis. AI integration of genomic, epigenetic, and metagenomic data may offer new classifications of gastrointestinal cancers and suggest optimal personalized treatments. In managing relapsing and remitting diseases such as inflammatory bowel disease, irritable bowel syndrome, and peptic ulcer bleeding, convoluted neural network may formulate models to predict disease outcome, enhancing treatment efficacy. AI and surgical robots can also assist surgeons in conducting gastrointestinal operations. While the advancement and new opportunities are exciting, the responsibility and liability issues of AI-assisted diagnosis and management need much deliberations.
Spinal surgery is a technically demanding and challenging procedure because of the complicated anatomical structures of the spine and its proximity to several important tissues. Surgical landmarks and fluoroscopy have been used for pedicle screw insertion but are found to produce inaccuracies in placement. Improving the safety and accuracy of spinal surgery has increasingly become a clinical concern. Computer-assisted navigation is an extension and application of precision medicine in orthopaedic surgery and has significantly improved the accuracy of spinal surgery. However, no clinical guidelines have been published for this relatively new and fast-growing technique, thus potentially limiting its adoption. In accordance with the consensus of consultant specialists, literature reviews, and our local experience, these guidelines include the basic concepts of the navigation system, workflow of navigation-assisted spinal surgery, some common pitfalls, and recommended solutions. This work helps to standardize navigation-assisted spinal surgery, improve its clinical efficiency and precision, and shorten the clinical learning curve.