MAIC-10 brief quality checklist for publications using artificial intelligence and medical images.

The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as "Clinical need", "Data annotation", "Robustness", and "Transparency" present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging.

Feasibility and outcomes of transjugular intrahepatic portosystemic shunts in infants.

BACKGROUND: Experience with transjugular intrahepatic portosystemic shunts (TIPS) in the pediatric population, especially in infants, is limited. OBJECTIVE: To evaluate the feasibility, efficacy and safety of TIPS placement in infants. MATERIALS AND METHODS: This retrospective non-comparative observational cohort study analyzed all pediatric patients < 12 months of age treated with TIPS while waiting for liver transplant between October 2018 and April 2021. The sample consisted of 10 infants with chronic liver disease. All had refractory ascites and decreased portal vein size. Their mean age ± standard deviation was 5 ± 1 months and their mean weight was 5.4 ± 1.0 kg. We calculated the pediatric end-stage liver disease score and portosystemic gradients before and after TIPS placement. We used ultrasound to check for complications and to assess the presence of ascites. We used paired-sample t-test for the mean comparison of paired variables. RESULTS: Ten TIPS procedures were performed that were technically and hemodynamically successful except for one, in which an extrahepatic portal puncture required surgical repair. Ascites resolved in three infants and was reduced in six. The portal vein size remained stable after TIPS placement. Four infants had early stent thrombosis and two had late stent thrombosis treated with angioplasty or covered stents. CONCLUSION: TIPS placement in infants is a feasible, safe and effective procedure.

DELEAT: gene essentiality prediction and deletion design for bacterial genome reduction.

BACKGROUND: The study of gene essentiality is fundamental to understand the basic principles of life, as well as for applications in many fields. In recent decades, dozens of sets of essential genes have been determined using different experimental and bioinformatics approaches, and this information has been useful for genome reduction of model organisms. Multiple in silico strategies have been developed to predict gene essentiality, but no optimal algorithm or set of gene features has been found yet, especially for non-model organisms with incomplete functional annotation. RESULTS: We have developed DELEAT v0.1 (DELetion design by Essentiality Analysis Tool), an easy-to-use bioinformatic tool which integrates an in silico gene essentiality classifier in a pipeline allowing automatic design of large-scale deletions in any bacterial genome. The essentiality classifier consists of a novel logistic regression model based on only six gene features which are not dependent on experimental data or functional annotation. As a proof of concept, we have applied this pipeline to the determination of dispensable regions in the genome of Bartonella quintana str. Toulouse. In this already reduced genome, 35 possible deletions have been delimited, spanning 29% of the genome. CONCLUSIONS: Built on in silico gene essentiality predictions, we have developed an analysis pipeline which assists researchers throughout multiple stages of bacterial genome reduction projects, and created a novel classifier which is simple, fast, and universally applicable to any bacterial organism with a GenBank annotation file.