Artificial intelligence reporting guidelines: what the pediatric radiologist needs to know

2021 ◽  
Author(s):  
Riwa Meshaka ◽  
Daniel Pinto Dos Santos ◽  
Owen J. Arthurs ◽  
Neil J. Sebire ◽  
Susan C. Shelmerdine
Keyword(s):  
Artificial Intelligence ◽  
Reporting Guidelines ◽  
Pediatric Radiologist

scholarly journals Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

2020 ◽  
Vol 2 (10) ◽  
pp. e537-e548 ◽  
Author(s):  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J Calvert ◽  
Alastair K Denniston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Reporting Guidelines

scholarly journals S591 Adherence to the SPIRIT-AI Standardized Reporting Guidelines in Artificial Intelligence Clinical Trials in Gastroenterology

2021 ◽  
Vol 116 (1) ◽  
pp. S268-S269
Author(s):  
Erik A. Holzwanger ◽  
Mohammad Bilal ◽  
Jeremy Glissen Brown ◽  
Ishita Barua ◽  
Mandeep S. Sawhney ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Reporting Guidelines ◽  
Standardized Reporting

scholarly journals Artificial intelligence in the endocrine clinic: automated bone age analysis in arabic children from UAE

2021 ◽  
Vol 8 (7) ◽  
pp. 418-422
Author(s):  
Nandu Kumar Thalange ◽  
Elham Elgabaly Moustafa Ahmed ◽  
Ajay Prasanth D'Souza ◽  
Mireille El Bejjani
Keyword(s):  
Artificial Intelligence ◽  
Retrospective Chart Review ◽  
Bone Age ◽  
Close Correlation ◽  
Pediatric Radiologist ◽  
Growth Disorders ◽  
X Rays ◽  
X Ray ◽  
Experienced Radiologist ◽  
High Utility

Objective: Artificial intelligence (AI) is playing an increasing role in patient assessment. AI bone age analysis is such a tool, but its value in Arabic children presenting to an endocrine clinic has not been explored. We compared results from an experienced pediatric radiologist and the AI bone age system, BoneXpert (BX), (Visiana, Denmark) to assess its utility in a cohort of children presenting to the Al Jalila Children’s Specialty Hospital endocrine service. Materials and Methods: We conducted a retrospective chart review of 47 children with growth disorders, initially assessed by a single experienced radiologist and subsequently by BX, to confirm the usefulness of the BX system in our population. The results of the analyses were analysed using a Bland-Altman plot constructed to compare differences between the radiologist’s interpretation and BX across the available range of bone age. Results: Forty-four of the patient x-ray images were analysed by BX. Three X-ray images were rejected by BX due to post-processing artifacts, which prevented computer interpretation. For the remaining 44 X-rays, there was a close correlation between radiologist and BX results (r=0.93; p <0.00001).  Two radiographs were identified with a large discrepancy in the reported bone ages. Blinded, independent re-evaluation of the radiographs showed the original manually interpreted bone age to have been erroneous, with the BX results corresponding closely to the amended bone age. A small positive bias was noted in bone age (+0.39 years) in the BX analyses, relative to manual interpretation. Conclusions: AI bone age analysis was of high utility in Arabic children from UAE presenting to an endocrine clinic, with results highly comparable to an experienced radiologist. In the two cases where a large discrepancy was found, independent re-evaluation showed AI analysis was correct.

scholarly journals Reporting Guidelines for Artificial Intelligence in Medical Research

Ophthalmology ◽  
2020 ◽  
Vol 127 (12) ◽  
pp. 1596-1599
Author(s):  
J. Peter Campbell ◽  
Aaron Y. Lee ◽  
Michael Abràmoff ◽  
Pearse A. Keane ◽  
Daniel S.W. Ting ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Research ◽  
Reporting Guidelines

scholarly journals Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hussein Ibrahim ◽  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
An-Wen Chan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
International Standards ◽  
Health Interventions ◽  
Reporting Guidelines ◽  
Traditional Health ◽  
Trial Protocols ◽  
International Experts

Abstract Background The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of ‘AI in health’ means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. Main text We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines—SPIRIT and CONSORT—are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. Conclusion The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.

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