Development and Verification of an Economical Method of Custom Target Library Construction

Miao, Xinyao; Li, Bowen; Shen, Yuesheng; Yu, Huiyun; Zhu, Guoqiang; Liang, Chen; Xiao, Fu; Wang, Chu; Li, Shengbin; Bao, Zhang

doi:10.1021/acsomega.0c01014

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…Chronological age estimation presents a key feature in forensic anthropology and evidence, especially in criminal investigations or disasters [ 8 , 16 , 19 , 38 , 55 , 56 , 64 , 66 ]. Despite the rapid advance of DNA sequencing technologies [ 41 ], Age determination with DNA methods is nontrivial. Due to higher juvenile criminals and diverse reduced or full age of criminal responsibility around different legal systems, children and teenagers’ age estimation through morphological methods is insufficient [ 10 , 61 ].…”

Section: Introductionmentioning

confidence: 99%

DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs

et al. 2022

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Background Age estimation from panoramic radiographs is a fundamental task in forensic sciences. Previous age assessment studies mainly focused on juvenile rather than elderly populations (> 25 years old). Most proposed studies were statistical or scoring-based, requiring wet-lab experiments and professional skills, and suffering from low reliability. Result Based on Soft Stagewise Regression Network (SSR-Net), we developed DENSEN to estimate the chronological age for both juvenile and older adults, based on their orthopantomograms (OPTs, also known as orthopantomographs, pantomograms, or panoramic radiographs). We collected 1903 clinical panoramic radiographs of individuals between 3 and 85 years old to train and validate the model. We evaluated the model by the mean absolute error (MAE) between the estimated age and ground truth. For different age groups, 3–11 (children), 12–18 (teens), 19–25 (young adults), and 25+ (adults), DENSEN produced MAEs as 0.6885, 0.7615, 1.3502, and 2.8770, respectively. Our results imply that the model works in situations where genders are unknown. Moreover, DENSEN has lower errors for the adult group (> 25 years) than other methods. The proposed model is memory compact, consuming about 1.0 MB of memory overhead. Conclusions We introduced a novel deep learning approach DENSEN to estimate a subject’s age from a panoramic radiograph for the first time. Our approach required less laboratory work compared with existing methods. The package we developed is an open-source tool and applies to all different age groups.

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Section: Introductionmentioning

confidence: 99%

DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs

et al. 2022

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“…Age estimation serves a key function in forensic anthropology and evidence, particularly in criminal investigations and disasters [ 3 , 4 ]. Despite rapid advances in DNA sequencing technology, age estimation using DNA methods is not generally available [ 40 ]. Therefore, age estimation using CNN through oral radiographs is highly likely to be used because it is easy to access.…”

Section: Discussionmentioning

confidence: 99%

Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks

Kim

Choi

et al. 2023

Healthcare

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Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.

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“…DNA libraries were constructed using two‐step polymerase chain reaction (PCR), a method developed previously by our team based on DNA nanoball (DNB) technology and two‐step PCR [22]. Briefly, sequencing was performed on the BGISEQ‐500 platform by constructing a single‐stranded DNA (ssDNA) circle and a DNB following the BGISEQ‐500 protocol.…”

Section: Methodsmentioning

confidence: 99%

Sequenced‐based Rwanda population provides insights into demographic history

Miao,

Gong,

Paul

et al. 2023

Electrophoresis

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Rwanda is known as the heart of Africa, reflecting the history of the world. Colonization and genocide have led to Rwanda's existing genetic structure. Herein, we used massively parallel sequencing to analyze 296 loci in 185 Rwandans and constructed a database for Rwandan forensic data for the first time. We found the following results: First, forensic parameters demonstrated that all loci were highly informative and could be used for forensic identification and paternity tests in Rwandans. Second, we found that the differences in genetic background between Rwandans and other African populations were similar but slight, as indicated by the massively parallel sequencing panel. Rwandans belonged to the African population and were inseparable from populations from neighboring countries. Also, Rwandans were closer to the European and American populations because of colonization, war, and other reasons. There was no scientific basis for racial classification established by colonization. Further research still needs to be carried out on more loci and larger Rwandan samples.

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Development and Verification of an Economical Method of Custom Target Library Construction

Cited by 4 publications

References 44 publications

DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs

DENSEN: a convolutional neural network for estimating chronological ages from panoramic radiographs

Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks

Sequenced‐based Rwanda population provides insights into demographic history

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