Diagnostic dry bone histology in human paleopathology

Boer, Hans H. de; Merwe, Ashirwad

doi:10.1002/ca.22753

Cited by 14 publications

(4 citation statements)

References 85 publications

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“…Assessing bone fragility becomes forensically important if a trauma victim is claimed to be predisposed to fractures due to a bone disease or advanced age (Rutty et al, 2013). Certain fragility‐inducing pathological disorders such as rickets or osteomalacia (Teitelbaum, 1980), osteomyelitis (de Boer & Van der Merwe, 2016), metastatic bone disease (Buijs & van der Pluijm, 2009), osseous tumors (Klein & Siegal, 2006), Paget's disease of bone (Meunier, Coindre, Edouard, & Arlot, 1980), osteopetrosis (Semba, Ishigami, Sugihara, & Kitano, 2000), and osteogenesis imperfecta (Rauch, Travers, Parfitt, & Glorieux, 2000) have distinct histological presentations (Stout et al, 2019). Distinguishing between traumatic and accidental fractures is most difficult in the elderly, where fracture risk is high (Collins, 2006; Collins & Presnell, 2007; Dolinak, 2008; Murphy, Waa, Jaffer, Sauter, & Chan, 2013; Rosen et al, 2016; Rosenblatt, Cho, & Durance, 1996).…”

Section: Nonosteonal Cortical Histomorphometry: Proxies Of Aged and Fmentioning

confidence: 99%

Current and emerging histomorphometric and imaging techniques for assessingage‐at‐deathand cortical bone quality

Andronowski

Cole

2020

WIREs Forensic Science

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Bones are dynamic living organs that undergo continual change throughout life. An internal process of tissue renewal, called remodeling, removes mature microscopic packets of bone, and replaces them with new bone in a highly coordinated manner. To date, it remains difficult to directly observe and track individual remodeling events in cortical bone due to the small size of the structures involved. High‐resolution imaging techniques hold the potential to provide novel three‐dimensional information pertaining to changes in bone's microarchitecture, cortical porosity, and the remodeling process. This review critically explores the methodological approaches used historically by researchers to assess the products of remodeling within cortical bone and relate it to age‐at‐death estimation, extending from histology to modern ex vivo imaging modalities, and discusses the growing potential of in vivo imaging. We further provide an introduction to various histological indicators of bone quality and fragility, their forensic relevance, and examples of novel imaging modalities employed for their investigation. The review concludes with an introduction to cutting‐edge in vivo four‐dimensional imaging techniques that include the use of animal models to shed new light on the dynamic nature of bone, and the processes of bone aging and disease. Data gleaned from these new insights will ultimately lead to the development of future histologic age‐estimation methods in forensic anthropology. This article is categorized under: Forensic Anthropology > Age Assessment Forensic Chemistry and Trace Evidence > Emerging Technologies and Methods Forensic Medicine > Imaging Modalities

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Section: Nonosteonal Cortical Histomorphometry: Proxies Of Aged and Fmentioning

confidence: 99%

Current and emerging histomorphometric and imaging techniques for assessingage‐at‐deathand cortical bone quality

Andronowski

Cole

2020

WIREs Forensic Science

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“…Additionally, in a diseased individual, presence of one illness can decrease immune function increasing risk of coinfection. If the coinfecting organism also involves the skeletal system, bone morphology and pattern of lesion location may reflect neither of the infections adequately for definitive diagnosis (Christensen, Martínez‐Lavín, & Peneda, ; de Boer & Van der Merwe, ). Thus, any estimate in the archaeological record must be considered an underestimate of the true prevalence of brucellosis.…”

Section: Brucellosis and Human Palaeopathologymentioning

confidence: 99%

Approaching ancient disease from a One Health perspective: Interdisciplinary review for the investigation of zoonotic brucellosis

Bendrey

Cassidy

Fournié

et al. 2019

Intl J of Osteoarchaeology

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Today, brucellosis is the most common global bacterial zoonosis, bringing with it a range of significant health and economic consequences, yet it is rarely identified from the archaeological record. Detection and understanding of past zoonoses could be improved by triangulating evidence and proxies generated through different approaches. The complex socioecological systems that support zoonoses involve humans, animals, and pathogens interacting within specific environmental and cultural contexts, and as such, there is a diversity of potential datasets that can be targeted. To capture this, in this paper, we consider how to approach the study of zoonotic brucellosis in the past from a One Health perspective, one which explicitly acknowledges the health link between people, animals, and environments (both physical and cultural). One Health research is explicitly interdisciplinary and conceptually moves away from an anthropocentric approach, allowing the component parts to be considered in holistic and integrated ways to deliver more comprehensive understanding. To this end, in this paper, we review the methods, selected evidence, and potential for past brucellosis identification and understanding, focussing on osteological markers in humans and animals, historical, biomolecular, and epidemiological approaches. We also present an agenda and potential for future research.

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“…From the bone tissue types, the outer highly mineralized layer of tissue provides resistance to further taphonomic alterations [ 3 ]. Thus, cortical bone histomorphometry can assist the disciplines offering insights into age-at-death, differentiation between animals and humans, and the identification of pathological conditions [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept

Littek

McKenna

Chiam

et al. 2023

Biology

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Cortical bone microstructure assessment in biological and forensic anthropology can assist with the estimation of age-at-death and animal-human differentiation, for example. Osteonal structures within cortical bone are the key feature under analysis, with osteon frequency and metric parameters providing crucial information for the assessment. Currently, the histomorphological assessment consists of a time-consuming manual process for which specific training is required. Our work investigates the feasibility of automatic analysis of human bone microstructure images through the application of deep learning. In this paper, we use a U-Net architecture to address the semantic segmentation of such images into three classes: intact osteons, fragmentary osteons, and background. Data augmentation was used to avoid overfitting. We evaluated our fully automatic approach using a sample of 99 microphotographs. The contours of intact and fragmentary osteons were traced manually to provide ground truth. The Dice coefficients were 0.73 for intact osteons, 0.38 for fragmented osteons, and 0.81 for background, giving an average of 0.64. The Dice coefficient of the binary classification osteon-background was 0.82. Although further refinement of the initial model and tests with larger datasets are needed, this study provides, to the best of our knowledge, the first proof of concept for the use of computer vision and deep learning for differentiating both intact and fragmentary osteons in human cortical bone. This approach has the potential to widen and facilitate the use of histomorphological assessment in the biological and forensic anthropology communities.

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Diagnostic dry bone histology in human paleopathology

Cited by 14 publications

References 85 publications

Current and emerging histomorphometric and imaging techniques for assessingage‐at‐deathand cortical bone quality

Current and emerging histomorphometric and imaging techniques for assessingage‐at‐deathand cortical bone quality

Approaching ancient disease from a One Health perspective: Interdisciplinary review for the investigation of zoonotic brucellosis

Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept

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