Age estimation [editorial].

yesAssessing and interpreting dental and skeletal age-related changes in both the living and the dead is of interest to a wide range of disciplines (e.g. see Bittles and Collins 1986) including human biology, paediatrics, public health, palaeodemography, archaeology, palaeontology, human evolution, forensic anthropology and legal medicine. ... This special issue of Annals of Human Biology arises from the 55th annual symposium of the Society for the Study of Human Biology in association with the British Association for Biological Anthropological and Osteoarchaeology held in Oxford, UK, from 9–11 December 2014. Only a selection of the presentations are included here which encompass some of the major recent advances in age estimation from the dentition and skeleton

Forensic age diagnostics by magnetic resonance imaging of the proximal humeral epiphysis

The most commonly used radiological method for age estimation of living individuals is X-ray. Computed tomography is not commonly used due to high radiation exposure, which raises ethical concerns. This problem can be solved with the use of magnetic resonance imaging (MRI), which avoids the use of ionizing radiation. The purpose of the present study was to evaluate the utility of MRI analysis of the proximal humeral epiphyses for forensic age estimations of living individuals. In this study, 395 left proximal humeral epiphyses (patient age 12-30years) were evaluated with fast-spin-echo proton density-weighted image (FSE PD) sequences in a coronal oblique orientation on shoulder MRI images. A five-stage scoring system was used following the method of Dedouit et al. The intra- and interobserver reliabilities assessed using Cohen's kappa statistic were =0.818 and =0.798, respectively. According to this study, stage five first appeared at 20 and 21years of age in males and females, respectively. These results are not directly comparable to any other published study due to the lack of MRI data on proximal humeral head development. These findings may provide valuable information for legally important age thresholds using shoulder MRI. The current study demonstrates that MRI of the proximal humerus can support forensic age estimation. Further research is needed to establish a standardized protocol that can be applied worldwide

Applicability of T1-weighted MRI in the assessment of forensic age based on the epiphyseal closure of the humeral head

This work investigates the value of magnetic resonance imaging analysis of proximal epiphyseal fusion in research examining the growth and development of the humerus and its potential utility in establishing forensic age estimation. In this study, 428 proximal humeral epiphyses (patient age, 12-30years) were evaluated with T1-weighted turbo spin echo (T1 TSE) sequences in coronal oblique orientation on shoulder MRI images. A scoring system was created following a combination of the Schmeling and Kellinghaus methods. Spearman's rank correlation analysis revealed a significant positive relationship between age and ossification stage of the proximal humeral epiphysis (all subjects: rho=0.664, p<0.001; males: 0.631, p<0.001; females: rho=0.651, p<0.001). The intra- and inter-observer reliability assessed using Cohen's kappa statistic was =0.898 and =0.828, respectively. The earliest age of epiphysis closure was 17years for females and 18years for males. MRI of the proximal humeral epiphysis can be considered advantageous for forensic age estimation of living individuals in a variety of situations, ranging from monitoring public health to estimating the age of illegal immigrants/asylum seekers, minors engaged in criminal activities, and illegal participants in competitive sports, without the danger of radiation exposure

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning

BACKGROUND: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4−6-week remission following a first episode of psychosis. METHOD: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts. RESULTS: Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability. CONCLUSIONS: Using items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians’ assessment should be undertaken to evaluate the possible utility as a routine clinical tool

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning

Background: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis.Method: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts.Results: Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naive. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P &lt;.0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P &lt;.0001), demonstrating reliability.Conclusions: Using items from common and validated clinical scales, our model significantly predicted early remission in patients with firstepisode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians’ assessment should be undertaken to evaluate the possible utility as a routine clinical tool

Towards precision medicine in psychosis: Benefits and challenges of multimodal multicenter studies - PSYSCAN: Translating neuroimaging findings from research into clinical practice

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures