Are semi-automated software program designed for adults accurate for the identification of vertebral fractures in children?

Reliability of semi-automated spinal measurement software

PURPOSE

In the treatment of patients with adult spinal deformity, analysis of spinopelvic balance is essential in clinical assessment and surgical planning. There is currently no gold standard for measurement, whether done by hand or with digital software. New semi-automated software exists that purports to increase efficiency, but its reliability is unknown in the literature.

METHODS

Full spine X-rays were retrospectively reviewed from 25 adult patients seen between 2014 and 2017. Patients were included if they had > 5 cm of sagittal imbalance and radiographs of sufficient quality to perform balance measurements, without prior surgical spinal fusion and/or instrumentation. Spinopelvic parameters were measured in two radiographic programs: one with basic, non-spine-specific measurement tools (eUnity, Client Outlook, Waterloo, Canada); and a second with spine-specific semi-automated measurement tools (Sectra, Sectra AB, Linköping, Sweden). Balance parameters included SVA, PI, PT, and LL. Data were compared by examining inter-rater and inter-program reliability using interclass correlation coefficient (ICC).

RESULTS

The subjects' mean age was 67.9 ± 13.8 years old, and 32% were male. The inter-program reliability was strong, with ICC values greater than 0.91 for each parameter. Similarly, there was strong inter-observer reliability with ICC values greater than 0.88. These results persisted on delayed repeat measurement (p < 0.001 for all measurements).

CONCLUSION

There is excellent inter-observer and inter-program reliability between the basic PACS and semi-automated programs. These data demonstrate that the purported efficiency of semi-automated measurement programs does not come at the cost of measurement reliability.

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis

Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.

Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum focused on pediatric radiology is needed to offer targeted education material on AI model development and performance evaluation. There are inherent differences between pediatric and adult practice patterns, which may hinder the application of adult AI models in pediatric cohorts. Such differences include the different imaging modality utilization, imaging acquisition parameters, lower radiation doses, the rapid growth of children and changes in their body composition, and the presence of unique pathologies and diseases, which differ in prevalence from adults. Thus, to enhance radiologists' knowledge of the applications of AI models in pediatric patients, curricula should be structured keeping in mind the unique pediatric setting and its challenges, along with methods to overcome these challenges, and pediatric-specific data governance and ethical considerations. In this report, the authors highlight the salient aspects of pediatric radiology that are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation.

The current and future roles of artificial intelligence in pediatric radiology

Artificial intelligence (AI) is a broad and complicated concept that has begun to affect many areas of medicine, perhaps none so much as radiology. While pediatric radiology has been less affected than other radiology subspecialties, there are some well-developed and some nascent applications within the field. This review focuses on the use of AI within pediatric radiology for image interpretation, with descriptive summaries of the literature to date. We highlight common features that enable successful application of the technology, along with some of the limitations that can inhibit the development of this field. We present some ideas for further research in this area and challenges that must be overcome, with an understanding that technology often advances in unpredictable ways.

Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs

Artificial intelligence (AI) applied to pediatric chest radiographs are yet scarce. This study evaluated whether AI-based software developed for adult chest radiographs can be used for pediatric chest radiographs. Pediatric patients (≤ 18 years old) who underwent chest radiographs from March to May 2021 were included retrospectively. An AI-based lesion detection software assessed the presence of nodules, consolidation, fibrosis, atelectasis, cardiomegaly, pleural effusion, pneumothorax, and pneumoperitoneum. Using the pediatric radiologist’s results as standard reference, we assessed the diagnostic performance of the software. For the total 2273 chest radiographs, the AI-based software showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 67.2%, 91.1%, 57.7%, 93.9%, and 87.5%, respectively. Age was a significant factor for incorrect results (odds radio 0.821, 95% confidence interval 0.791–0.851). When we excluded cardiomegaly and children 2 years old or younger, sensitivity, specificity, PPV, NPV and accuracy significantly increased (86.4%, 97.9%, 79.7%, 98.7% and 96.9%, respectively, all p < 0.001). In conclusion, AI-based software developed with adult chest radiographs showed diagnostic accuracies up to 96.9% for pediatric chest radiographs when we excluded cardiomegaly and children 2 years old or younger. AI-based lesion detection software needs to be validated in younger children.

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

BACKGROUND

Majority of research and commercial efforts have focussed on use of artificial intelligence (AI) for fracture detection in adults, despite the greater long-term clinical and medicolegal implications of missed fractures in children. The objective of this study was to assess the available literature regarding diagnostic performance of AI tools for paediatric fracture assessment on imaging, and where available, how this compares with the performance of human readers.

MATERIALS AND METHODS

MEDLINE, Embase and Cochrane Library databases were queried for studies published between 1 January 2011 and 2021 using terms related to 'fracture', 'artificial intelligence', 'imaging' and 'children'. Risk of bias was assessed using a modified QUADAS-2 tool. Descriptive statistics for diagnostic accuracies were collated.

RESULTS

Nine eligible articles from 362 publications were included, with most (8/9) evaluating fracture detection on radiographs, with the elbow being the most common body part. Nearly all articles used data derived from a single institution, and used deep learning methodology with only a few (2/9) performing external validation. Accuracy rates generated by AI ranged from 88.8 to 97.9%. In two of the three articles where AI performance was compared to human readers, sensitivity rates for AI were marginally higher, but this was not statistically significant.

CONCLUSIONS

Wide heterogeneity in the literature with limited information on algorithm performance on external datasets makes it difficult to understand how such tools may generalise to a wider paediatric population. Further research using a multicentric dataset with real-world evaluation would help to better understand the impact of these tools.

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

Artificial intelligence in paediatric radiology: international survey of health care professionals' opinions

BACKGROUND

The nature of paediatric radiology work poses several challenges for developing and implementing artificial intelligence (AI) tools, but opinions of those working in the field are currently unknown.

OBJECTIVE

To evaluate the attitudes and perceptions toward AI amongst health care professionals working within children's imaging services.

MATERIALS AND METHODS

A web-based questionnaire was distributed to the membership of several paediatric and general radiological societies over a 4-month period (1 Feb - 31 May 2020). Survey questions covered attitudes toward AI in general, future impacts and suggested areas for development specifically within paediatric imaging.

RESULTS

Two hundred and forty responses were collected with the majority being from radiologists (159/240, 66.3%; 95% confidence interval [CI] 59.8-72.2%) or allied health care professionals (72/240, 31.3%; 95% CI 25.4-37.5%). Respondents agreed that AI could potentially alert radiologists to imaging abnormalities (148/240, 61.7%; 95% CI 55.2-67.9%) but preferred that results were checked by a human (200/240, 83.3%; 95% CI 78.0-87.8%). The majority did not believe jobs in paediatric radiology would be replaced by AI (205/240, 85.4%; 95% CI 80.3-89.6%) and that the development of AI tools should focus on improved diagnostic accuracy (77/240, 32.1%; 95% CI 26.2-38.4%), workflow efficiencies (60/240, 25.0%; 95% CI 19.7-30.9%) and patient safety (54/240, 22.5%; 95% CI 17.4-28.3%). The majority of European Society of Paediatric Radiology (ESPR) members (67/81, 82.7%; 95% CI 72.7-90.2%) welcomed the idea of a dedicated paediatric radiology AI task force with emphasis on educational events and anonymised dataset curation.

CONCLUSION

Imaging health care professionals working with children had a positive outlook regarding the use of AI in paediatric radiology, and did not feel their jobs were threatened. Future AI tools would be most beneficial for easily automated tasks and most practitioners welcomed the opportunity for further AI educational activities.

[Artificial intelligence in image evaluation and diagnosis]

Die Radiologie ist von stetem Wandel geprägt und definiert sich über den technologischen Fortschritt. Künstliche Intelligenz (KI) wird die praktische Tätigkeit in der Kinder- und Jugendradiologie künftig in allen Belangen verändern. Bildakquisition, Befunderkennung und -segmentierung sowie die Erkennung von Gewebeeigenschaften und deren Kombination mit Big Data werden die Haupteinsatzgebiete in der Radiologie sein. Höhere Effektivität, Beschleunigung von Untersuchung und Befundung sowie Kosteneinsparung sind mit der Anwendung von KI verbundene Erwartungshaltungen. Ein verbessertes Patientenmanagement, Arbeitserleichterungen für medizinisch-technische Radiologieassistenten und Kinder- und Jugendradiologen sowie schnellere Untersuchungs- und Befundzeiten markieren die Meilensteine der KI-Entwicklung in der Radiologie. Von der Terminkommunikation und Gerätesteuerung bis zu Therapieempfehlung und -monitoring wird der Alltag durch Elemente der KI verändert. Kinder- und Jugendradiologen müssen daher grundlegend über KI informiert sein und mit Datenwissenschaftlern bei der Etablierung und Anwendung von KI-Elementen zusammenarbeiten.

Dual-energy X-ray absorptiometry bone densitometry in pediatrics: a practical review and update

The assessment of pediatric bone mineral content and density is an evolving field. In this manuscript we provide a practical review and update on the interpretation of dual-energy X-ray absorptiometry (DXA) in pediatrics including historical perspectives as well as a discussion of the recently published 2019 Official Position Statements of the International Society of Clinical Densitometry (ISCD) that apply to children.

Artificial intelligence in paediatric radiology: Future opportunities

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.