Probabilistic age classification with Bayesian networks: a study on the ossification status of the medial clavicular epiphysis

In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

Arterial Therapies of Non-Colorectal Liver Metastases.

BACKGROUND: The unique situation of the liver with arterial and venous blood supply and the dependency of the tumor on the arterial blood flow make this organ an ideal target for intrahepatic catheter-based therapies. Main forms of treatment are classical bland embolization (TAE) cutting the blood flow to the tumors, chemoembolization (TACE) inducing high chemotherapy concentration in tumors, and radioembolization (TARE) without embolizing effect but very high local radiation. These different forms of therapies are used in different centers with different protocols. This overview summarizes the different forms of treatment, their indications and protocols, possible side effects, and available data in patients with non-colorectal liver tumors. METHODS: A research in PubMed was performed. Mainly clinical controlled trials were reviewed. The search terms were 'embolization liver', 'TAE', 'chemoembolization liver', 'TACE', 'radioembolization liver', and 'TARE' as well as 'chemosaturation' and 'TACP' in the indications 'breast cancer', 'neuroendocrine', and 'melanoma'. All reported studies were analyzed for impact and reported according to their clinical relevance. RESULTS: The main search criteria revealed the following results: 'embolization liver + breast cancer', 122 results, subgroup clinical trials 16; 'chemoembolization liver + breast cancer', 62 results, subgroup clinical trials 11; 'radioembolization liver + breast cancer', 37 results, subgroup clinical trials 3; 'embolization liver + neuroendocrine', 283 results, subgroup clinical trials 20; 'chemoembolization liver + neuroendocrine', 202 results, subgroup clinical trials 9; 'radioembolization liver + neuroendocrine', 64 results, subgroup clinical trials 9; 'embolization liver + melanoma', 79 results, subgroup clinical trials 15; 'chemoembolization liver + melanoma', 60 results, subgroup clinical trials 14; 'radioembolization liver + melanoma', 18 results, subgroup clinical trials 3. The term 'chemosaturation liver' was tested without indication since only few publications exist and provided us with five results and only one clinical trial. CONCLUSION: Despite many years of clinical use and documented efficacy on intra-arterial treatments of the liver, there are still only a few prospective multicenter trials with many different protocols. To guarantee the future use of these efficacious therapies, especially in the light of many systemic or surgical therapies in the treatment of non-colorectal liver metastases, further large randomized trials and transparent guidelines need to be established.

Probabilistic graphical models to deal with age estimation of living persons

Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

How to report multiple outcome metrics in virtual reality simulation

Background Virtual reality (VR) simulation is increasingly used in surgical disciplines. Since VR simulators measure multiple outcomes, standardized reporting is needed. Methods We present an algorithm for combining multiple VR outcomes into dimension summary measures, which are then integrated into a meaningful total score. We reanalyzed the data of two VR studies applying the algorithm. Results The proposed algorithm was successfully applied to both VR studies. Conclusions The algorithm contributes to standardized and transparent reporting in VR-related research.