Clinical experience on the limited role of ultrasound for breast cancer screening in BRCA1 and BRCA2 mutations carriers aged 30-39 years.

PURPOSE: In BRCA germline pathogenic sequence variants (PSV) carriers aged 30-39 years imaging is recommended at six-month intervals. The European society for medical oncology recommendation of the use of 6-monthly MRI six-monthly MRI screening is being considered at our institution, particularly for younger carriers under the age of 35, although it is not mandatory. If 6-monthly MRI is unavailable, annual MRI may be supplemented by ultrasound (with or without mammography). The aim of this study was to evaluate the utility of ultrasound screening added to mammography, as a 6-month supplement to annual MRI in BRCA PSV carriers aged 30-39 years. MATERIALS AND METHODS: This IRB approved retrospective study included BRCA PSV carriers aged 30-39 years, who underwent breast cancer screening at our institution between January 2015 and March 2023. Participants were divided into two groups, those who had supplemental whole-breast US and mammography at six months and underwent screening before March 2019, and those who had only mammography without supplemental US and enrolled in screening after March 2019. Patient characteristics, cancer detection rates and cancer characteristics were compared between the two groups. RESULTS: Overall, 200 asymptomatic BRCA1/2 PSV carriers undergoing screening in our institution were included in the study. Mean age was 35.7 ± 3.5 years, and mean follow-up time was 37.4 ± 38.0 months. There were 118 (59 %) women screened with supplemental US, and 82 (41 %) women without. Eight cancers were diagnosed during the study period, four in women with supplemental US and four in women without. The sensitivity of whole-breast screening US was 25 % (1/4), specificity 85.7 % (222/259), PPV 2.6 % (1/38), and NPV 98.7 % (222/225). Of the four cancers detected in women screened with supplemental US, one was diagnosed by whole-breast US, two by MRI, and one by mammography. Of eight cancers included in this study, two were not detectable by targeted second-look US. All eight cancers were detectable by MRI. CONCLUSION: The addition of whole-breast ultrasound to mammography and MRI screening in BRCA PSV carriers aged 30-39 years offered limited incremental benefit. MRI with 6 months supplemental mammography without US detected all cancer cases.

Utilizing large language models in breast cancer management: systematic review.

PURPOSE: Despite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field. METHODS: We searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". The risk bias was evaluated using the QUADAS-2 tool. RESULTS: Six studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information. CONCLUSION: LLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.

Findings on emergent magnetic resonance imaging in pregnant patients with suspected appendicitis: A single center perspective.

This study's aim is to describe the imaging findings in pregnant patients undergoing emergent MRI for suspected acute appendicitis, and the various alternative diagnoses seen on those MRI scans. This is a single center retrospective analysis in which we assessed the imaging, clinical and pathological data for all consecutive pregnant patients who underwent emergent MRI for suspected acute appendicitis between April 2013 and June 2021. Out of 167 patients, 35 patients (20.9%) were diagnosed with acute appendicitis on MRI. Thirty patients (18%) were diagnosed with an alternative diagnosis on MRI: 17/30 (56.7%) patients had a gynecological source of abdominal pain (e.g. ectopic pregnancy, red degeneration of a leiomyoma); 8 patients (26.7%) had urological findings such as pyelonephritis; and 6 patients (20%) had gastrointestinal diagnoses (e.g. abdominal wall hernia or inflammatory bowel disease). Our conclusions are that MRI is a good diagnostic tool in the pregnant patient, not only in diagnosing acute appendicitis, but also in providing information on alternative diagnoses to acute abdominal pain. Our findings show the various differential diagnoses on emergent MRI in pregnant patients with suspected acute appendicitis, which may assist clinicians and radiologists is patient assessment and imaging utilization.

Assessment of glymphatic dysfunction in pediatric idiopathic intracranial hypertension: insights from quantitative diffusivity and perivascular spaces analysis-a case-control study.

Background: The impaired drainage of cerebrospinal fluid through the glymphatic system is thought to play a role in the idiopathic intracranial hypertension (IIH) pathophysiology. Limited data exist regarding the glymphatic system's involvement in pediatric patients with IIH. Therefore, the study's objective was to quantitatively evaluate alterations in parenchymal diffusivity and magnetic resonance imaging (MRI)-visible dilated perivascular spaces (PVS) as imaging indicators of glymphatic dysfunction in pediatric patients with IIH. Methods: Patients diagnosed with IIH in 2017-2022 in a single tertiary center (Sheba Medical Center, Israel) were retrospectively reviewed. Twenty-four pediatric patients were enrolled. All patients underwent clinical 3-T brain MRI. The control group included 24 age- and gender-matched healthy subjects with a normal-appearing brain on imaging. We used automatic atlas-based diffusion-weighted imaging analysis to determine regional diffusivity of the thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, and brain stem. PVS were evaluated using a semi-quantitative rating scale on T2-weighted images. Variables were compared using the Mann-Whitney test. Multivariate analysis of covariance was used to test for differences between controls and IIH patients. Results: No significant differences in regional brain diffusivity were observed between individuals with IIH and healthy controls (P=0.14-0.91 for various brain regions). The number of visible PVS was comparable between patients with IIH and the control group across all evaluated sites (P=0.12-0.74 for various brain regions). Conclusions: Pediatric IIH patients exhibited similar patterns of parenchymal diffusivity and PVS compared to age-matched controls. These findings do not support the hypothesis that the glymphatic system may play a role in the pathophysiology of pediatric IIH, although previously postulated. However, employing more sophisticated magnetic resonance (MR) techniques could enhance the sensitivity in uncovering underlying glymphatic dysfunction. Further research is warranted to validate and explore this association in larger cohorts and investigate the underlying mechanisms involved in IIH.

Adversarial attacks in radiology - A systematic review.

PURPOSE: The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology. METHODS: We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases. RESULTS: A total of 22 studies published between March 2018 and April 2023 were included, primarily focused on image classification algorithms. Fourteen studies evaluated white-box attacks, three assessed black-box attacks and five investigated both. Eleven of the 22 studies targeted chest X-ray classification algorithms, while others involved chest CT (6/22), brain MRI (4/22), mammography (2/22), abdominal CT (1/22), hepatic US (1/22), and thyroid US (1/22). Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100 %. CONCLUSIONS: Adversarial attacks are a growing concern. Although currently the threats are more theoretical than practical, they still represent a potential risk. It is important to be alert to such attacks, reinforce cybersecurity measures, and influence the formulation of ethical and legal guidelines. This will ensure the safe use of deep learning technology in medicine.

Large language model (ChatGPT) as a support tool for breast tumor board.

Large language models (LLM) such as ChatGPT have gained public and scientific attention. The aim of this study is to evaluate ChatGPT as a support tool for breast tumor board decisions making. We inserted into ChatGPT-3.5 clinical information of ten consecutive patients presented in a breast tumor board in our institution. We asked the chatbot to recommend management. The results generated by ChatGPT were compared to the final recommendations of the tumor board. They were also graded independently by two senior radiologists. Grading scores were between 1-5 (1 = completely disagree, 5 = completely agree), and in three different categories: summarization, recommendation, and explanation. The mean age was 49.4, 8/10 (80%) of patients had invasive ductal carcinoma, one patient (1/10, 10%) had a ductal carcinoma in-situ and one patient (1/10, 10%) had a phyllodes tumor with atypia. In seven out of ten cases (70%), ChatGPT's recommendations were similar to the tumor board's decisions. Mean scores while grading the chatbot's summarization, recommendation and explanation by the first reviewer were 3.7, 4.3, and 4.6 respectively. Mean values for the second reviewer were 4.3, 4.0, and 4.3, respectively. In this proof-of-concept study, we present initial results on the use of an LLM as a decision support tool in a breast tumor board. Given the significant advancements, it is warranted for clinicians to be familiar with the potential benefits and harms of the technology.

Large language models for oncological applications.

Large language models such as ChatGPT have gained public and scientific attention. These models may support oncologists in their work. Oncologists should be familiar with large language models to harness their potential while being aware of potential dangers and limitations.

Ultrasound-Tomographic Image Fusion - A Novel Tool for Follow up After Acute Complicated Appendicitis.

BACKGROUND: Computerized tomography (CT) is an integral part of the follow-up and decision-making process in complicated acute appendicitis (AA) treated non-operatively. However, repeated CT scans are costly and cause radiation exposure. Ultrasound-tomographic image fusion is a novel tool that integrates CT images to an Ultrasound (US) machine, thus allowing accurate assessment of the healing process compared to CT on presentation. In this study, we aimed to assess the feasibility of US-CT fusion as part of the management of appendicitis. MATERIALS AND METHODS: We retrospectively collected data of consecutive patients with complicated AA managed non-operatively and followed up with US Fusion for clinical decision-making. Patients demographics, clinical data, and follow-up outcomes were extracted and analyzed. RESULTS: Overall, 19 patients were included. An index Fusion US was conducted in 13 patients (68.4%) during admission, while the rest were performed as part of an ambulatory follow-up. Nine patients (47.3%) had more than 1 US Fusion performed as part of their follow-up, and 3 patients underwent a third US Fusion. Eventually, 5 patients (26.3%) underwent elective interval appendectomy based on the outcomes of the US Fusion, due to a non-resolution of imaging findings and ongoing symptoms. In 10 patients (52.6%), there was no evidence of an abscess in the repeated US Fusion, while in 3 patients (15.8%), it significantly diminished to less than 1 cm in diameter. CONCLUSION: Ultrasound-tomographic image fusion is feasible and can play a significant role in the decision-making process for the management of complicated AA.

Cardiovascular Risk Factors and Physical Fitness Among Subjects with Asymptomatic Colonic Diverticulosis.

BACKGROUND: The association between diverticular disease and atherosclerotic cardiovascular disease (ASCVD) has been demonstrated previously, mainly in symptomatic subjects. AIMS: To evaluate 10 years cardiovascular risk, exercise performance and association to ASCVD among subjects with asymptomatic diverticulosis. METHODS: A retrospective cross-sectional cohort of self-referred participants in a medical screening program, who underwent a screening colonoscopy. Demographics, clinical and laboratory variables, ASCVD score, and metabolic equivalents (METs) during treadmill stress test were compared between subjects with and without diverticulosis as diagnosed on screening colonoscopy. RESULTS: 4586 participants underwent screening colonoscopy; 799 (17.4%) had diverticulosis. Among 50-69 yo participants, diverticulosis subjects had a higher ASCVD score compared to non-diverticulosis subjects. Exercise performance was comparable between the groups, across all age groups. Using logistic regression analysis, advanced age group (50-59 yo Adjusted odds ratio (AOR) [95% confidence interval (CI)] 2.57 (1.52-4.34), p < 0.001; 60-69 yo, AOR 2.87 (2.09-3.95), p < 0.001; ≥ 70 yo AOR 4.81 (3.23-7.15), p < 0.001; compared to < 50 yo age group), smoking [AOR 1.27 (1.05-1.55), p = 0.016], HTN [AOR 1.27 (1.03-1.56), p = 0.022], obesity [AOR 1.36 (1.06-1.74), p = 0.014] and male sex [AOR 1.29 (1.02-1.64), p = 0.036] were associated with diverticular detection during screening colonoscopy. Among males, achieving METs score ≥ 10 was inversely associated with diverticular detection during screening colonoscopy [AOR 0.64 (0.43-0.95), p = 0.027]. CONCLUSIONS: Ten years probability for ASCVD estimated by the ASCVD score is higher among subjects with asymptomatic diverticulosis compared to subjects without diverticulosis. Improved exercise performance is demonstrated for the first time to correlate with decreased probability for diverticular disease in screening colonoscopy.

Artificial intelligence for identification of focal lesions in intraoperative liver ultrasonography.

PURPOSE: Intraoperative ultrasonography (IOUS) of the liver is a crucial adjunct in every liver resection and may significantly impact intraoperative surgical decisions. However, IOUS is highly operator dependent and has a steep learning curve. We describe the design and assessment of an artificial intelligence (AI) system to identify focal liver lesions in IOUS. METHODS: IOUS images were collected during liver resections performed between November 2020 and November 2021. The images were labeled by radiologists and surgeons as normal liver tissue versus images that contain liver lesions. A convolutional neural network (CNN) was trained and tested to classify images based on the labeling. Algorithm performance was tested in terms of area under the curves (AUCs), accuracy, sensitivity, specificity, F1 score, positive predictive value, and negative predictive value. RESULTS: Overall, the dataset included 5043 IOUS images from 16 patients. Of these, 2576 were labeled as normal liver tissue and 2467 as containing focal liver lesions. Training and testing image sets were taken from different patients. Network performance area under the curve (AUC) was 80.2 ± 2.9%, and the overall classification accuracy was 74.6% ± 3.1%. For maximal sensitivity of 99%, the classification specificity is 36.4 ± 9.4%. CONCLUSIONS: This study provides for the first time a proof of concept for the use of AI in IOUS and show that high accuracy can be achieved. Further studies using high volume data are warranted to increase accuracy and differentiate between lesion types.

Risk factors and prediction algorithm for advanced neoplasia on screening colonoscopy for average-risk individuals.

Background: Screening with colonoscopy for all average-risk population is probably not cost-effective due to the limited sources and over-generalization of the risk, and risk stratification can be used to optimize colorectal cancer screening. Objectives: We aimed to assess risk factors for advanced neoplasia (AN) and a classification tree algorithm to predict the risk. Design: This is a retrospective cross-sectional study. Methods: This study was composed of consecutive asymptomatic average-risk individuals undergoing first screening colonoscopy between 2008 and 2019. Detailed characteristics including background diseases, habits, and medications were collected. We used multivariable logistic regression to investigate the associations between clinical variables and the presence of AN and built a classification algorithm to predict AN. Results: A total of 3856 patients were included (73.2% male, median age 55). Adenoma and AN detection rate were 15.8% and 3.4%, respectively. On multivariable analysis, predictors of AN [odds ratio (OR), 95% confidence interval (CI)] were age (1.04, 1.01-1.06, p = 0.003), male sex (2.69, 1.56-4.64, p < 0.001), and smoking (1.97, 1.38-2.81, p < 0.001). A classification tree algorithm showed that smoking was the most important risk factor for prediction of AN (4.9% versus 2.4%, p < 0.001), followed by age with a cutoff value of 60 in the smokers (8.4% versus 3.8%, p = 0.001) and 50 in the non-smokers (2.9% versus 0.9%, p = 0.004). Conclusion: Smoking habits, old age, and male gender are highly associated with an increased risk for AN and should be incorporated in the individualized risk-assessment to adapt a screening program.

Changes in Helicobacter pylori Treatment from Discovery to Nowadays: A High-Level Analysis of PubMed Publications.

Background: Since the discovery of Helicobacter pylori (H. pylori) 40 years ago, treatment options have changed dramatically adjacent to new drugs development and the emergence of multi-drug resistance. In this study, we aimed to assess published literature on treatment regimens for H. pylori in the past four decades, with regards to specific drug resistance and geographic distribution. Methods: We have queried PubMed for all available H. pylori-related entries published during 1987-2020. For each entry, we retrieved the title, abstract, and keywords. Two gastrointestinal specialists decided in consensus on a list of terms to classify entries. The terms specify types of treatment and different types of regimens. Annual trends of publications were plotted for different treatment types and sub-analyses. Results: Overall, 47,170 H. pylori-related entries were published between 1987 and 2020. Medication treatment showed the highest number of total publications 6183/47,170 (13.1%), followed by antibiotics 5493/47,170 (11.6%), and probiotic which was substantially lower (0.7%). For treatment regimens type, triple therapy had the highest number of publications 2400/47,170 (5.1%) followed by sequential therapy 342/47,170 (0.7%), and bismuth quadruple therapy 115/47,170 (0.2%). Analysis by antibiotic resistance showed that clarithromycin treatment had the highest number of publications 3537/47,170 (7.5%). Since 2013, there is a steep upward slope (5.1±0.7 publications/year, p < 0.001) for publications originating from China. Conclusion: Our findings reflect the changes of the commonly prescribed regimens along the years, following the occurrence and the rise of H. pylori antibiotic resistance. In recent years, there is a steep rise in publications in countries with policies of screening and eradication for gastric cancer prevention, which is currently not applied in Western countries. A text-mining analysis of H. pylori publications contributes to the understanding of treatment options and development trends worldwide.

Alerting on mortality among patients discharged from the emergency department: a machine learning model.

OBJECTIVES: Physicians continuously make tough decisions when discharging patients. Alerting on poor outcomes may help in this decision. This study evaluates a machine learning model for predicting 30-day mortality in emergency department (ED) discharged patients. METHODS: We retrospectively analysed visits of adult patients discharged from a single ED (1/2014-12/2018). Data included demographics, evaluation and treatment in the ED, and discharge diagnosis. The data comprised of both structured and free-text fields. A gradient boosting model was trained to predict mortality within 30 days of release from the ED. The model was trained on data from the years 2014-2017 and validated on data from the year 2018. In order to reduce potential end-of-life bias, a subgroup analysis was performed for non-oncological patients. RESULTS: Overall, 363 635 ED visits of discharged patients were analysed. The 30-day mortality rate was 0.8%. A majority of the mortality cases (65.3%) had a known oncological disease. The model yielded an area under the curve (AUC) of 0.97 (95% CI 0.96 to 0.97) for predicting 30-day mortality. For a sensitivity of 84% (95% CI 0.81 to 0.86), this model had a false positive rate of 1:20. For patients without a known malignancy, the model yielded an AUC of 0.94 (95% CI 0.92 to 0.95). CONCLUSIONS: Although not frequent, patients may die following ED discharge. Machine learning-based tools may help ED physicians identify patients at risk. An optimised decision for hospitalisation or palliative management may improve patient care and system resource allocation.

Trends in inflammatory bowel disease treatment in the past two decades-a high-level text mining analysis of PubMed publications.

AIM: Many therapeutic options for inflammatory bowel disease (IBD) emerged during the last 2 decades, along with the rise in disease prevalence and incidence. We aimed at assessing the published literature on different treatment options in that period. Special attention was attributed to specific medication mechanisms and geographic diversity. MATERIALS AND METHODS: We have queried PubMed for all available IBD-related entries published during 2000-2020. The following data were extracted for each entry: PubMed unique article ID (PMID), title, publishing journal, abstract text, keywords (if any), and authors' affiliations. Two gastrointestinal specialists decided in consensus on a list of terms to classify entries. The terms belonged to five treatment groups: medical, surgical, dietary, microbiome manipulation, and complementary medicine. The medical and complementary medicine groups were further sub-classified. Annual trends of publications for the years 2000-2020 were plotted for different treatment types. The slopes of publication trends were calculated by fitting regression lines to the annual number of publications. RESULTS: Overall, 77,505 IBD entries were published between 2000 and 2020. Medical treatment showed the highest number of total publications 21,540/77,505 (27.8%), followed by surgical 7605/77,505 (9.8%), microbiome research 5260/77,505 (6.8%), dietary 4819/77,505 (6.2%), and complementary medicine treatment 762/77,505 (1.0%). Interestingly, since 2012 there is a steep rise in microbiome publications that outnumbered surgery in the last 2 years. Trend analysis of medical treatment showed that biologics had the steepest slope (57.5, p < 0.001), followed by immunomodulators (4.9, p < 0.001), small molecules (3.9, p < 0.001), and 5-ASA (3.8, p < 0.001). CONCLUSION: According to our high-level publications trend analysis, the past 2 decades certainly deserve the reference as the "biologic era", as publications regarding biological therapy outnumbered all other treatment options. Interestingly, though very popular among patients, complementary medicine was not studied with correlation to its' acceptance among patients.

Management of Acute Appendicitis during the COVID-19 Pandemic: A Single Tertiary Center Experience.

BACKGROUND: The coronavirus disease-2019 (COVID-19) outbreak had an effect on healthcare. OBJECTIVES: To evaluate the presentation and management of patients with acute appendicitis. METHODS: A retrospective study was conducted of all patients presenting with acute appendicitis to the emergency department of a large tertiary center during March and April 2020. Clinical features, diagnostic workup, and management were compared. RESULTS: Seventy-four patients presented with acute appendicitis during the pandemic compared to 60 patients during the same time the year before. There were no significant differences in patient demographics: age (P = 0.65), gender (P = 0.73), smoking status (P = 0.48). During COVID-19 patients were more likely to complain of right lower quadrant pain (100% vs. 78.3%, P < 0.01). Rates of surgical treatment was similar (83.8% vs. 81.7%, P = 1); mean operative time was longer during COVID-19 (63 ± 23 vs. 52 ± 26 minutes, P = 0.03). There were no significant differences in intra-operative findings including the presence of appendiceal perforation (16.3% vs. 14.5%, P = 0.8), abscess (6.1% vs. 9.7%, P = 0.73), or involvement of cecum or terminal ileum (14.28% vs. 19.63%, P = 1). Postoperative treatment with antibiotics was more prevalent during COVID-19 (37.1% vs. 18%, P = 0.04). Length of stay (1.82 ± 2.04 vs. 2.74 ± 4.68, P = 0.2) and readmission rates (6% vs. 11.3%, P =0.51) were similar. CONCLUSIONS: The COVID-19 pandemic did not significantly affect the presentation, clinical course, management, and outcomes of patients presenting with acute appendicitis.

Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.

BACKGROUND AND AIM: Endoscopic differentiation between malignant and benign gastric ulcers (GU) affects further evaluation and prognosis. The aim of our study was to evaluate a deep learning algorithm for discrimination between benign and malignant GU in a database of endoscopic ulcer images. METHODS: We retrospectively collected consecutive upper gastrointestinal endoscopy images of GU performed between 2011 and 2019 at the Sheba Medical Center. All ulcers had a corresponding histopathology result of either benign peptic ulcer or gastric adenocarcinoma. A convolutional neural network (CNN) was trained to classify the images into either benign or malignant. Endoscopies from 2011 to 2017 were used for training (2011-2015) and validation (2016-2017). Hyper-parameters, image augmentation and pre-training on Google images obtained images were evaluated on the validation data. Held-out data from 2018 to 2019 was used for testing the final model. RESULTS: Overall, the Sheba dataset included 1978 GU images; 1894 images from benign GU and 84 images of malignant ulcers. The final CNN model showed an AUC 0.91 (95% CI 0.85-0.96) for detecting malignant ulcers. For cut-off probability 0.5, the network showed a sensitivity of 92% and specificity of 75% for malignant ulcers. CONCLUSION: Our study displays the applicability of a CNN model for automated evaluation of gastric ulcers images for malignant potential. Following further research, the algorithm may improve accuracy of differentiating benign from malignant ulcers during endoscopies and assist in patients' stratification, allowing accelerated patients management and individualized approach towards surveillance endoscopy.

Deep learning for noninvasive liver fibrosis classification: A systematic review.

BACKGROUND AND AIMS: While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analysis. This technology has the potential to enhance noninvasive fibrosis assessment. We systematically examined the application of DL in noninvasive liver fibrosis imaging. METHODS: Embase, MEDLINE, Web of Science, and IEEE Xplore databases were used to identify studies that reported on the accuracy of DL for classification of liver fibrosis on noninvasive imaging. The search keywords were "liver or hepatic," "fibrosis or cirrhosis," and "neural or DL networks." Risk of bias and applicability were evaluated using the QUADAS-2 tool. RESULTS: Sixteen studies were retrieved. Imaging modalities included ultrasound (n = 10), computed tomography (n = 3), and magnetic resonance imaging (n = 3). The studies analyzed a total of 40 405 radiological images from 15 853 patients. All but two of the studies were retrospective. In most studies the "ground truth" reference was the METAVIR score for pathological staging (n = 9.56%). The majority of the studies reported an accuracy >85% when compared to histopathology. Fourteen studies (87.5%) had a high risk of bias and concerns regarding applicability. CONCLUSIONS: Deep learning has the potential to play an emerging role in liver fibrosis classification. Yet, it is still limited by a relatively small number of retrospective studies. Clinicians should facilitate the use of this technology by sharing databases and standardized reports. This may optimize the noninvasive evaluation of liver fibrosis on a large scale.

Deep learning visual analysis in laparoscopic surgery: a systematic review and diagnostic test accuracy meta-analysis.

BACKGROUND: In the past decade, deep learning has revolutionized medical image processing. This technique may advance laparoscopic surgery. Study objective was to evaluate whether deep learning networks accurately analyze videos of laparoscopic procedures. METHODS: Medline, Embase, IEEE Xplore, and the Web of science databases were searched from January 2012 to May 5, 2020. Selected studies tested a deep learning model, specifically convolutional neural networks, for video analysis of laparoscopic surgery. Study characteristics including the dataset source, type of operation, number of videos, and prediction application were compared. A random effects model was used for estimating pooled sensitivity and specificity of the computer algorithms. Summary receiver operating characteristic curves were calculated by the bivariate model of Reitsma. RESULTS: Thirty-two out of 508 studies identified met inclusion criteria. Applications included instrument recognition and detection (45%), phase recognition (20%), anatomy recognition and detection (15%), action recognition (13%), surgery time prediction (5%), and gauze recognition (3%). The most common tested procedures were cholecystectomy (51%) and gynecological-mainly hysterectomy and myomectomy (26%). A total of 3004 videos were analyzed. Publications in clinical journals increased in 2020 compared to bio-computational ones. Four studies provided enough data to construct 8 contingency tables, enabling calculation of test accuracy with a pooled sensitivity of 0.93 (95% CI 0.85-0.97) and specificity of 0.96 (95% CI 0.84-0.99). Yet, the majority of papers had a high risk of bias. CONCLUSIONS: Deep learning research holds potential in laparoscopic surgery, but is limited in methodologies. Clinicians may advance AI in surgery, specifically by offering standardized visual databases and reporting.

Postoperative outcomes following inguinal hernia repair in inflammatory bowel disease patients compared to matched controls.

OBJECTIVES: To assess surgical outcome in inflammatory bowel disease (IBD) patients who underwent inguinal hernia repair and to asses possible risk factors. METHODS: A retrospective analysis of a prospective database including all IBD patients treated in a large tertiary center between 2008 and 2019 was conducted. IBD patients who underwent inguinal hernia surgery were matched using a propensity match scoring based on demographic and perioperative characteristics. Clinical operative data were extracted from medical records and analyzed. RESULTS: Overall, out of 5467 IBD patients treated in our institute, 26 patients (0.47%) underwent inguinal hernia repair. Seventy-six matched patients with similar characteristics were compared to the IBD group. Postoperative complications were found to be more common in the IBD group (30.7% vs 11.8%; P = 0.03) compared to controls. We found no significant differences in length of stay (3.38 vs 2.83 days; P = 0.21) and hernia recurrence rate (7.6% vs 9.2%; P = 1). Within the IBD group, multivariate analysis failed to demonstrate any possible risk factor for postoperative complications, including gender [-1.53 to 2.81 95% confidence interval (CI), P = 0.52], age (-0.34 to 1.15 95% CI, P = 0.25), BMI (-0.041 to 0.019 95% CI, P = 0.43), American Society of Anesthesiologists score (-0.15 to 0.54 95% CI, P = 0.24) or medications (-0.25 to 0.28 95% CI, P = 0.88). In addition, out of various operative factors, including operation urgency, surgical approach and surgery duration, only the latter was found to be correlated with postoperative complications (0.013-0.035 95% CI, P < 0.001). CONCLUSION: IBD Patients undergoing abdominal wall hernia surgery are prone to more postoperative complications.

Normal-range emergency department serum phosphorus levels and all-cause mortality.

PURPOSE OF THE STUDY: Hypophosphataemia and hyperphosphataemia are frequently encountered in hospitalised patients and are associated with significant clinical consequences. However, the prognostic value of normal-range phosphorus levels on all-cause mortality and hospitalisations is not well established. Therefore, we examined the association between normal-range phosphorus levels, all-cause mortality and hospitalisations in patients presenting to the emergency department of a tertiary medical centre in Israel. STUDY DESIGN: A retrospective analysis of patients presenting to the Chaim Sheba Medical Center emergency department between 2012 and 2018. The cohort was divided into quartiles based on emergency department phosphorus levels: 'very-low-normal' (p ≥ 2 mg/dL and p ≤ 2.49 mg/dL), 'low-normal' (p ≥ 2.5 mg/dL and p ≤ 2.99 mg/dL), 'high-normal' (p≥  3 mg/dL and p≤3.49 mg/dL) and 'very-high-normal' (p ≥  3.5 mg/dL and p ≤ 4 mg/dL). We analysed the association between emergency department phosphorus levels, hospitalisation rate and 30-day and 90-day all-cause mortality. RESULTS: Our final analysis included 223 854 patients with normal-range phosphorus levels. Patients with 'very-low-normal' phosphorus levels had the highest mortality rate. Compared with patients with 'high-normal' phosphorus levels, patients with 'very-low-normal' levels had increased 30-day all-cause mortality (OR 1.3, 95% CI 1.1 to 1.4, p<0.001), and increased 90-day all-cause mortality (OR 1.2, 95% CI 1.1 to 1.3, p<0.001). Lower serum phosphorus levels were also associated with a higher hospitalisation rate, both for the internal medicine and general surgery wards (p<0.001). CONCLUSIONS: Lower phosphorus levels, within the normal range, are associated with higher 30-day and 90-day all-cause mortality and hospitalisation rate.