Enhanced Recovery After Surgery (ERAS) is an evidence-based paradigm shift in perioperative care, proven to lower both recovery time and postoperative complication rates. The role of ERAS in several surgical disciplines was reviewed. In colorectal surgery, ERAS protocol is currently well established as the best care. In gastric surgery, 2014 saw an establishment of ERAS protocol for gastrectomies with resulting meta-analysis showing ERAS effectiveness. ERAS has also been shown to be beneficial in liver surgery with many centers starting implementation. The advantages of ERAS in pancreatic surgery have been strongly established, but there is still a need for large-scale, multicenter randomized trials. Barriers to implementation were analyzed, with recent studies concluding that successful implementation requires a multidisciplinary team, a willingness to change and a clear understanding of the protocol. Additionally, the difficulty in accomplishing necessary compliance to all protocol items calls for new implementation strategies. ERAS success in different patient populations was analyzed, and it was found that in the elderly population, ERAS shortened the length of hospitalization and did not lead to a higher risk of postoperative complications or readmissions. ERAS utilization in the emergency setting is possible and effective; however, certain changes to the protocol may need to be adapted. Therefore, further research is needed. There remains insufficient evidence on whether ERAS actually improves patients’ course in the long term. However, since most centers started to implement ERAS protocol less than 5 years ago, more data are expected.

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

PurposeThree-dimensional (3D) printing for preoperative planning has been intensively developed in the recent years. However, the implementation of these solutions in hospitals is still difficult due to high costs, extremely expensive industrial-grade printers, and software that is difficult to obtain and learn along with a lack of a defined process. This paper presents a cost-effective technique of preparing 3D-printed liver models that preserves the shape and all of the structures, including the vessels and the tumor, which in the present case is colorectal liver metastasis.MethodsThe patient’s computed tomography scans were used for the separation and visualization of virtual 3D anatomical structures. Those elements were transformed into stereolithographic files and subsequently printed on a desktop 3D printer. The multipart structure was assembled and filled with silicone. The patient underwent subsequent laparoscopic right hemihepatectomy. The entire process is described step-by-step, and only free-to-use and mostly open-source software was used.ResultsAs a result, a transparent, full-sized liver model with visible vessels and colorectal metastasis was created for under $150, which—taking into account 3D printer prices—is much cheaper than models presented in previous research papers.ConclusionsThe increased accessibility of 3D models for physicians before complex laparoscopic surgical procedures such as hepatic resections could lead to beneficial breakthroughs in these sophisticated surgeries, as many reports show that these models reduce operative time and improve short term outcomes.

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.