Researchers Take Aim at Gun Violence
Written by Chris Bain   

CONFRONTING GUN VIOLENCE and the massive toll it takes on a daily basis remains front-and-center in public life. Mass shootings may grab the most headlines, but law enforcement knows it is the steady stream of individual cases that contribute to the United States’ disproportionately high rate of gun violence. Even when there are no fatalities, each incident of gun violence is a tragedy that has devastating consequences and changes life forever for those caught in its aftermath.

Fortunately, there is a consensus that employing smart technology to improve the capabilities of law enforcement is a common-sense approach everyone can agree on. A study by the United States Sentencing Commission found that gun offenders are statistically the most likely to recommit crimes, with 45.5 percent of firearms offenders being re-convicted versus only 27.6 percent for other offenders. This increases the urgency of bringing individual firearms offenders to justice.

While roughly 20 percent of gun murders are solved within 24 hours, the overall likelihood of solving a firearm homicide sits at just 46 percent, significantly lower than the 75 percent rate for non-firearm homicides. Even more disturbing is evidence showing that the chances of solving a shooting is actually declining. Equipping investigators with the tools they need to solve cases quickly not only increases their likelihood of success but also helps them take dangerous suspects off the street before they offend again.

Around much of the country, police departments are overburdened, their capabilities limited by budget cuts and ever-expanding mandates. To make headway in the fight against crime, it is more necessary than ever before to make sure that law enforcement works smarter, not just harder. New digital tools represent a unique opportunity for police to stay one step ahead and make real progress in securing communities nationwide.

Machines that Learn
While TV and film may glamorize old-fashioned police work, today’s investigators know that innovative scientific approaches can make or break a case. Just as DNA testing made a rapid leap from the university lab to the crime lab, the convergence of 3D modeling, machine learning, and augmented reality has the potential to take cutting-edge science and apply it in the real world. A team from Monash University – Australia’s largest research university – is at the forefront of that effort.

By analyzing and compiling a subset of over 75,000 postmortem computed tomography (CT) scans from the Victorian Institute of Forensic Medicine (VIFM), the Monash team is working to apply machine learning to create a digital 3D model of human anatomy that is capable of identifying entry and exit wounds in shooting victims, giving law enforcement instant insight that can be used in their investigation. By recording the trajectory of the projectile through the body (identifying and localizing projectile fragments in the process) the scan can determine key information, such as whether the wounds were self-inflicted.

The VIFM includes Monash University’s Department of Forensic Medicine and performs autopsy services for all deaths reported to the Victorian State Coroner. With an archive that increases by 7,000 cases a year, all causes of death are represented in the comprehensive database, including traumatic injury, homicide, and suicide. The unrivaled scope of the project means that all ages, ethnic groups, and genders are represented within the archive, making it an extremely useful tool moving forward.

The size and scope of the database does present a drawback to those seeking to harness the full extent of its insights. Containing more than two million digital photographic images, including external and internal injuries, pathological conditions and external whole-body images, means that comprehensive analysis of its entire content is well beyond human capabilities. Machine learning, however, has the potential to analyze and catalog these images in a fraction of the time that it would take human experts.

3D Visualization in Real Life — and Augmented Life
This is a massive step forward in a field that has employed the same observer-dependent basic analytic techniques for much of the last 100 years.

Before the advent of CT imaging, x-rays were used to produce a 2D view of the subject, which made localizing projectiles and fragments difficult without conducting an internal examination. Typically, the trajectory of foreign objects is determined using long probes to assess a projectile’s path. Another significant drawback is that current imaging techniques can’t differentiate between bullet fragments and other foreign metal objects, such as pacemakers or dental fillings.

Monash Forensic researchers, working in conjunction with Monash Faculty of IT experts in AI, are making 3D digital reconstructions of shooting victims. These reconstructions allow investigators to view victims along multiple planes and from different vantage points, using advanced computer graphics and augmented reality. Advanced machine learning can then be applied to determine trajectory and projectile fragmentation, resulting in a 3D-printed model that can potentially be submitted as evidence in court.

When the scans are run thorough machine-learning algorithms, researchers will be able to recognize repeating patterns from observing what happens when different types of bullets strike the body. This will make it possible for investigators to understand the type of weapon that was used, even if no bullet fragments are recovered and if there is no evidence found at the crime scene.

With this knowledge, law enforcement will eventually be able to compare the weapon and ammunition used with other crimes and also run results through a weapons database that will help them to quickly identify potential subjects.

Once fully realized, the scan will also be able to determine how the gun was held as it was fired, the range from which the gun was fired, and even the height of the shooter – all of which could expedite autopsies and help investigative efforts.

Visualizing the Benefits Ahead
Despite underpinnings of scientific rigor, modern forensic evidence can run into problems in the courtroom since its interpretation often hinges on the subjective opinions of experts. Recent studies have called into question many of the long-established assumptions that had lent credibility to forensic techniques, such as the analysis of hair samples, bite marks, and blood spatter.

Human interpretation opens the door to legal attacks on the objectivity and validity of the evidence being presented. Eliminating subjectivity also eliminates potential biases.

This multi-disciplinary team of scientists at Monash University is working to outsource analysis from the subjective human brain to advanced machine learning algorithms that will allow investigators to approach cases with greater objectivity and neutrality.

Gaining a better understanding of exactly how bullets interact with the tissue they come into contact with may also expand the range of applications for this technology. For example, team members envision the 3D scan as a useful aid in military frontline care. Leidos — a leading international IT and engineering company — is helping to fund the work through the Monash Institute of Medical Engineering, with ultimate military health applications in mind.

When fully developed, this scan alone will be able to reduce the need for comprehensive autopsies, allowing police to better allocate resources by differentiating between murders and suicides while also boosting the veracity of evidence used in court. By providing instant insight into the type of weapon used and an analysis of the bullet’s trajectory, law enforcement will be immediately equipped with everything that they need to begin their investigation on the right foot.

Gun violence shows humanity at its lowest, but the scientific ideal that keep us pushing for progress and knowledge show us at our best. Integrating scientific approaches into crime investigations has yielded spectacular results and has helped bring justice in cases that would have otherwise gone cold long ago. The 3D scanning technology will continue this legacy.

The togetherness, unity, and solidarity amid gun tragedies offers a blueprint for how Americans can unite to solve this pressing issue. Innovative scientific breakthroughs like the 3D scan from Monash University won’t end crime but they will go a long way in making communities safer and more secure.

About the Author
Dr. Chris Bain is the inaugural Professor of Practice in Digital Health at Monash University in the Faculty of Information Technology. He has more than 25 years of experience in the health industry, including 12 in clinical medicine. He’s led numerous software development and implementations projects in the clinical and management support areas, resulting in a range of prototype and fully implemented systems. A large part of his work involves leading the university's initiatives in digital health, working in collaboration with other faculties.

This article appeared in the January-February 2020 issue of Evidence Technology Magazine.
You can view that issue here.

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