Artificial intelligence in colorectal surgery: an AI-powered systematic review.

Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.

Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures.

PURPOSE: There is emerging evidence that radiomics analyses can improve detection of skeletal fragility. In this cross-sectional study, we evaluated radiomics features (RFs) on computed tomography (CT) images of the lumbar spine in subjects with or without fragility vertebral fractures (VFs). METHODS: Two-hundred-forty consecutive individuals (mean age 60.4 ± 15.4, 130 males) were evaluated by radiomics analyses on opportunistic lumbar spine CT. VFs were diagnosed in 58 subjects by morphometric approach on CT or XR-ray spine (D4-L4) images. DXA measurement of bone mineral density (BMD) was performed on 17 subjects with VFs. RESULTS: Twenty RFs were used to develop the machine learning model reaching 0.839 and 0.789 of AUROC in the train and test datasets, respectively. After correction for age, VFs were significantly associated with RFs obtained from non-fractured vertebrae indicating altered trabecular microarchitecture, such as low-gray level zone emphasis (LGLZE) [odds ratio (OR) 1.675, 95% confidence interval (CI) 1.215-2.310], gray level non-uniformity (GLN) (OR 1.403, 95% CI 1.023-1.924) and neighboring gray-tone difference matrix (NGTDM) contrast (OR 0.692, 95% CI 0.493-0.971). Noteworthy, no significant differences in LGLZE (p = 0.94), GLN (p = 0.40) and NGDTM contrast (p = 0.54) were found between fractured subjects with BMD T score < - 2.5 SD and those in whom VFs developed in absence of densitometric diagnosis of osteoporosis. CONCLUSIONS: Artificial intelligence-based analyses on spine CT images identified RFs associated with fragility VFs. Future studies are needed to test the predictive value of RFs on opportunistic CT scans in identifying subjects with primary and secondary osteoporosis at high risk of fracture.

Clinical efficacy of implementing a Patient Blood Management (PBM) Protocol in joint replacement surgery: a retrospective cohort study in a national referral center.

OBJECTIVE: The aging of population has dramatically broadened the total number of Total Hip Arthroplasty (THA) and Total Knee Arthroplasty (TKA) performed worldwide. To optimize the number of blood transfusions performed, a multimodal and multidisciplinary approach was introduced, called Patient Blood Management (PBM). The aim of the present retrospective study is to evaluate the feasibility and clinical outcomes of a PBM protocol applied in a national referral center for joint replacement surgery. PATIENTS AND METHODS: Clinical reports of 9,635 patients undergoing primary THA or TKA, from 2014 to 2019, were screened. The trends of hemoglobin value at admission and at day 4 after surgery were analyzed. Furthermore, the trend of blood bags' requests and blood transfusions was longitudinally evaluated to assess the efficacy of our PBM protocol and its potential impact in reducing the length of stay in the hospital. RESULTS: In 2014, mean hemoglobin (Hb) levels at postoperative day 4 were 10.3 g/dl and 10.2 g/dl for TKA (unilateral and bilateral, respectively), and in 2019 were 11.3 g/dl and 11.6 g/dl (unilateral and bilateral, respectively, p=0.001). Total requested red blood cell (RBC) transfusions by each surgery over time have decreased for THA (277 in 2014 vs. 120 in 2019, p=0.001).  A correlation matrix analysis between Hb level, body mass index (BMI), age, days spent in orthopedic (OR) ward and number of requested transfusions showed that RBC bags transfusions were related to the length of the hospital stay. CONCLUSIONS: A timely application of a PBM protocol in the perioperative period of TKA and THA was significantly associated to the reduction of blood transfusions and total length of hospital stay, with clear benefits for both the patients and the hospital.