Identification of Personality Based on the Sphenoid Sinus Structure - Using Machine Learning

Aug 4, 2026

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Study develops a new, simple machine-learning method to identify personality by analyzing characteristics of the sphenoid sinus structure, intended for routine medical practice in Ukraine. The research uses 200 multislice computed tomography (MSCT) scans from individuals of different genders and ages. Results achieve accuracy exceeding 70%, supporting the feasibility of automated analysis and identity establishment when conventional forensic options are limited.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Introduction

Background and need for identification

Proposed approach using CT and segmentation

Challenges and limitations

Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning Alina Nechyporenkoa,b, Marcus Frohmeb , Vladyslav Omelchenkob, Victoriia Alekseevab,c,d, Andrii Lupyrc and Vitaliy Garginc,d a b Kharkiv National University of Radioelectronics, Nauky avenue 14, Kharkiv, 61166, Ukraine Technical University of Applied Sciences Wildau (TH Wildau), Hochschulring 1, Wildau, 15745, Germany c Kharkiv National Medical University, Nauky avenue 4, Kharkiv, 61022, Ukraine d Kharkiv International Medical University, Molochna street 38, Kharkiv, 61001, Ukraine Abstract The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70% . Keywords 1 Identification of personality, multislice computed tomography, deep learning 1. Introduction The full-scale Russian invasion has had a profound impact on Ukrainian society, presenting numerous challenges and causing immense suffering for millions of Ukrainians [1] . War crimes continue to be committed in the occupied territories, and the discovery of unmarked mass graves in various locations is deeply disturbing [2] . Establishing the identities of those who have fallen victim to Russian aggression is a crucial task at hand [3] . Personal identification is particularly important during times of war, although no method can guarantee a 100% reliable result [1] . Fingerprints [4] are commonly used, but in wartime, bodies may be burned or damaged, rendering fingerprint identification impossible. Autolysis, the natural decomposition of bodies over time, can also hinder the use of fingerprints or retinas for identification. DNA identification [5] is a promising and accurate method, but obtaining DNA samples from deceased individuals' relatives is not always feasible. While global databases exist for DNA identification, Ukraine lacks such a resource. Additionally, DNA collection requires significant time, effort, and invasiveness, making implementation challenging. The proposed study aims to utilize existing data for collection and analysis. Bones are considered the most stable structures for the analysis [6], and studying cranial bones, particularly the sphenoid sinus, shows promise. Computed tomography [7] (CT) scans can be used to examine the sphenoid sinus, as it is less likely to be damaged due to its deep location within the skull. Medical image segmentation [8], a process that identifies pixels of interest in medical images, can be employed to process CT images. Convolutional neural networks (CNNs), particularly the U-Net architecture, have proven effective for medical image segmentation. CNNs offer promising potential for automated diagnostic methods and personal identification with help of deep learning [9] . While there are existing segmentation platforms, a clear workflow and necessary functions for easy IDDM’2023: 6th International Conference on Informatics & Data-Driven Medicine, November 17-19, 2023, Bratislava, Slovakia EMAIL: [alinanechiporenko@gmail.com](alinanechiporenko@gmail.com); [mfrohme@th-wildau.de](mfrohme@th-wildau.de); [vladyslav.omelchenko3@nure.ua](vladyslav.omelchenko3@nure.ua); [vik1305230@gmail.com](vik1305230@gmail.com); [lupyr_ent@ukr.net](lupyr_ent@ukr.net); [vitgarg@ukr.net](vitgarg@ukr.net) ORCID: 0000-0001-9063-2682; 0000-0002-4501-7426; 0009-00057395-3982, 0000-0001-5272-8704; 0000–0002–9465–224X; 0000-0001- 8194-4019, ©􀀀 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org)) CEUR ceur-ws.org Works

FAQs

What is the main goal of the study?

To develop a new, simple and effective machine-learning method for identifying personality based on sphenoid sinus structure characteristics for later routine medical use in Ukraine.

What data was used for training and evaluation?

The study involved 200 multislice computed tomography (MSCT) scans from individuals of various genders and ages.

How accurate is the proposed method?

The reported results achieve accuracy exceeding 70%.

Aug 4, 2026

Upload by Noah