Lung cancer has brought tough challenges to human health due to its high incidence and mortality rate in the current practice. Nowadays, computed tomography (CT) imaging is still the most preferred diagnostic tool for early screening of lung cancer. However, a great challenge brought from accumulative CT imaging data can not meet the demand of the current clinical practice. As a novel kind of artificial intelligence technique aimed to deal with medical images, a computer-aided diagnosis has been found to provide useful auxiliary information, attenuate the workload of doctors, and significantly improve the efficiency and accuracy for clinical diagnosis of lung cancer. Therefore, an effective combination of computer-aided techniques and CT imaging has increasingly become an active area of investigation in early diagnosis of lung cancer. This review aims to summarize the latest progress on the diagnostic value of computer-aided technology with regard to early stage lung cancer from the perspectives of machine learning and deep learning.
Objective To analyze the major complications and predictive factors of amputees during postoperative hospitalization, and provide a reference for amputees nursing and early rehabilitation. Methods Using the bibliometric method, we searched Embase, Ovid, Medline, PubMed, CINAHL, China National Knowledge Infrastructure, Wanfang and CQVIP databases for the data of postoperative hospitalization of amputees published from January 1st, 2008 to April 5th, 2022. Statistical description and analysis of article types, sample size, reasons for amputation, amputation sites, complications, influencing factors, predictive factors, and treatment recommendations were performed.Results Finally, 19 articles were included, including 16 in English and 3 in Chinese, all of which were quantitative studies. The literature quality scores were greater than or equal to 7 points, which were all good or excellent. The type of articles were mainly retrospective research (n=15), and the research contents were mainly lower limb amputation. The main reasons for amputation were peripheral vascular disease and diabetes mellitus (n=11). Wound infection, anemia, phantom limb pain, and psychological problems were common complications after amputation. Predictors of complications, secondary operations, and death included age, gender, smoking, drinking, obesity, preoperative comorbidities, level of amputation, anesthesia methods and other factors. Conclusions The focus of acute care after amputation should be wound healing, pain control, proximal physical movement and emotional support, especially for amputees who have prominent postoperative psychological problems. These patients need early psychological disease screening and mental support. After amputation, multi-disciplinary and multi-team coordinated care are needed to achieve both physical and psychological healing of the patient and promote early recovery.