Forensic age estimation for pelvic X-ray images using deep learning

Li, Yuan; Huang, Zhizhong; Dong, Xingbei; Liang, Weibo; Hui-feng, Xue; Zhang, Lin; Zhang, Yi; Deng, Zhenhua

doi:10.1007/s00330-018-5791-6

Cited by 61 publications

(37 citation statements)

References 32 publications

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“…There is considerable interest in accelerating identification procedures, and experts are involved in machine learning in forensic procedures. They use X-ray images [108][109][110][111][112][113], MRI images [107,114], photography [115][116][117], CT scans [118][119][120][121][122][123] of the head or other bones such as the collarbone, femur, teeth, etc. and use databases to teach artificial intelligence to identify people's age or gender.…”

Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

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Three-dimensional convolutional neural networks (3D CNN) as a type of artificial intelligence (AI) are powerful in image processing and recognition using deep learning to perform generative and descriptive tasks. The advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision. 3D CNN are used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim of this article was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers. With emphasis activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance the forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore and present 3D CNN method for forensic research design concept in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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Section: Overview Of Used Artificial Intelligence For Forensic Age and Sex Determinationmentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine

Thurzo¹,

Kosnáčová²,

Kurilová³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The iliac crest apophysis provides an excellent subject for the application of forensic age diagnostics to the living, particularly for determining age thresholds of 14, 16 and 18 years. For this reason, Li et al [113] developed a DL system to perform automatic bone age estimation based on 1875 clinical X-ray pelvic radiological images, particularly for individuals between 10 and 25 years old. It can handle all possible cases of automated skeletal bone age assessment, even for samples from individuals of 19, 20, and 21 years old.…”

Section: Age Estimation From Skeletal Structuresmentioning

confidence: 99%

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

et al. 2020

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This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.

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“…In terms of the currently reported use of ML in forensic post-mortem imaging, it is in its infancy. ML has only been trialed in a few specific forensic applications including automatic forensic dental identification [ 36 ]; sex determination [ [37] , [38] , [39] ]; the automation of bone age assessment [ 40 , 41 ]; prediction of bone fractures [ 42 ]; and the automatic detection of hemorrhagic pericardial effusion [ 43 ]. As far as we are aware, none of these studies has translated into daily forensic practice, despite the potential to streamline case-work.…”

Section: Forensic Applicationsmentioning

confidence: 99%

“…Limitations of this work included the requirement for age-relevant anatomical information, which implies a labor-intensive pre-processing step, and decreased accuracy for cases with biological ages greater than 18 years. In an alternative approach, Li et al [ 41 ] utilized pelvic X-ray images and a DCNN to create a bone age assessment pipeline which yielded a mean error of 0.94 years, 0.36 years better than the existing reference standard. This work used transfer learning from a CNN pre-trained on the ImageNet database [ 44 ], achieving an appropriate accuracy for this type of input data.…”

Section: Forensic Applicationsmentioning

confidence: 99%

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

Peña-Solórzano

Albrecht

Bassed

et al. 2020

Forensic Science International

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Highlights Machine learning techniques are currently not widely used in addressing forensic imaging problems. Machine learning applications can be transferred from clinical to forensic settings. Convolutional neural networks are outperforming other machine learning techniques in image processing tasks. There is a dearth of quantitative metrics presented for inter-study comparisons. A wider use of histopathological gold standard metrics for assessing algorithm performance is required.

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Forensic age estimation for pelvic X-ray images using deep learning

Cited by 61 publications

References 32 publications

Use of Advanced Artificial Intelligence in Forensic Medicine

Use of Advanced Artificial Intelligence in Forensic Medicine

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting

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