Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm

Zhou, Yuwei; Teomete, Uygar; Dandin, Özgür; Osman, Onur; Dandinoglu, Taner; Bağcı, Ulaş; Zhao, Weizhao

doi:10.1016/j.compbiomed.2016.09.013

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…On the basis of study title and abstract, 318 articles were excluded or irretrievable. After review of the full text ( n = 44), eight studies were eventually included [ 17 , 30 – 36 ]. An additional search of the medical literature on 18 February 2022 revealed one additional study.…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence for radiological paediatric fracture assessment: a systematic review

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

“…Almost half of all studies had unclear/moderate concerns regarding applicability of patient selection (4/9, 44.4%) [ 31 , 34 , 36 , 37 ], and most had concerns regarding applicability of index test (6/9, 66.7%) [ 31 – 36 ]. This was predominantly due to studies imposing strict exclusion criteria in their patient selection (e.g.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

et al. 2022

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“…The program was able to theoretically compensate for and process poorlytaken radiographs. 14,15 However, at its current state of learning, the program has been 'taught' using only 400 images. This is below the 1,000-image minimum recommended by artificial neural network programmers for image identification.…”

Section: Discussionmentioning

confidence: 99%

Hip Fracture Detection Using Artificial Intelligence: A Pilot Study

Grozman¹,

Dumalo²,

Gonzales³

2021

Acta Med Philipp

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Background. Hip fractures are commonly missed on the first radiograph in up to 30% of patients. The delay in diagnosis leads to significant gaps in management and consequent morbidities. Thus, a computer-aided hip fracture recognition through the Artificial Neural Network deep learning model, which allows the program to learn and gain experience with more images processed, has been created. The study aimed to determine the accuracy and sensitivity of the artificial neural network model in detecting fractures of the hip and explored the feasibility of its use as a diagnostic screening tool. Materials and Methods. A sample size of 45 participants/samples per treatment group was computed using a confidence level of 90%, and prevalence of 0.05 for a pilot study. The program was tested by processing digital pictures of radiographs of patients with known hip fractures that included femoral neck, intertrochanteric, subtrochanteric, and proximal femur fractures taken from the database of adult patients, who have undergone surgery for a hip fracture at the Philippine General Hospital from 2016-17. The 90 (45 fractured, 45 normal) manually selected proximal femur images were run on 10 models. The models were based on AlexNet and VGG-16, which are the representative convoluted neural networks designed for image analysis. Results and Conclusion. The program had an accuracy of 70%, specificity of 42.2% and sensitivity of 97.8%. This study is proof of concept that a deep learning model fracture detection software shows potential in hip fracture detection. Further training is necessary to make this promising innovation clinically useful.

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“…Our interdisciplinary research group started working in the field of pediatric trauma computer vision applications in 2018 [52]. As described [90,91], AI can be helpful in the domain of automated fracture detection. One of the main hurdles in establishing AI algorithms is the lack of annotated training data sets and data quality.…”

Section: Personal Experiencesmentioning

confidence: 99%

The augmented radiologist: artificial intelligence in the practice of radiology

et al. 2021

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In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can “see” more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics — thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled “explainable AI,” which should enable a balance/cooperation between AI and human intelligence — thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks.

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Computer-Aided Detection (CADx) for Plastic Deformation Fractures in Pediatric Forearm

Cited by 10 publications

References 28 publications

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

Artificial intelligence for radiological paediatric fracture assessment: a systematic review

Hip Fracture Detection Using Artificial Intelligence: A Pilot Study

The augmented radiologist: artificial intelligence in the practice of radiology

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