AI in Forensic Sciences: Past, Present, and Future Applications

Introduction & Background

Forensic Pathology (FP) plays a crucial role in the administration of justice by aiding in crime detection and investigation. FP has evolved alongside medical and technological advancements, and one of the most groundbreaking innovations is the application of Artificial Intelligence (AI). First coined by John McCarthy in 1956, AI refers to the simulation of human intelligence by machines. The development of Machine Learning (ML) and Deep Learning (DL) has propelled AI into various sectors, including Forensic Sciences. This article systematically reviews AI’s impact on Forensic Pathology, from human identification to postmortem interval estimation and the determination of causes of death.

This article is based on the peer-reviewed study by Ioannis Ketsekioulafis, Giorgos Filandrianos, Konstantinos Katsos, Konstantinos Thomas, Chara Spiliopoulou, Giorgos Stamou, and Emmanouil I. Sakelliadis, published on September 28, 2024, in Cureus. Titled Artificial Intelligence in Forensic Sciences: A Systematic Review of Past and Current Applications and Future Perspectives (DOI: 10.7759/cureus.70363), the article offers an extensive review of how Artificial Intelligence (AI) has been integrated into forensic sciences, ranging from human identification to postmortem interval estimation, and cause of death determination.

In This Article:

Evolution of AI in Forensic Sciences

Artificial Intelligence first made its debut in the mid-20th century when John McCarthy coined the term in 1956. AI refers to the simulation of intelligent behavior by machines, and since then, its evolution has been rapid, especially in areas requiring complex data analysis.

Machine Learning (ML) was a significant milestone, marking the shift from static algorithms to systems that could “learn” from data without explicit programming. A key subset of ML, Deep Learning (DL), was introduced in 1986 by Rina Dechter, which took inspiration from the brain’s neural networks. Further developments followed, with Artificial Neural Networks (ANNs) being officially recognized in 2000 by Igor Aizenberg and colleagues.

In forensic sciences, these advancements have opened the door to automating processes like pattern recognition, image analysis, and predictive modeling, significantly reducing human error and speeding up forensic investigations.

AI in Forensic Pathology

The application of Artificial Intelligence in forensic pathology has been transformative in several areas:

1. Human Identification

Human identification is critical in forensic cases, especially when physical identification is impossible due to decomposition or trauma. Traditionally, methods like fingerprint analysis, DNA profiling, and dental records were used. Now, AI-based tools like DENT-net, a CNN (Convolutional Neural Network), have been introduced to streamline this process.

DENT-net analyzes panoramic radiographs (dental X-rays) to compare and match dental patterns, offering quick and accurate identification in cases where traditional methods may take longer or fail. AI’s application in this area is particularly useful in mass casualty events, where rapid identification of bodies is crucial.

In forensic anthropology, AI has been employed to estimate the age and sex of skeletal remains. AI systems can process radiographic images, such as pelvic X-rays, to determine biological markers that indicate age, sex, and other identifiers. In many cases, AI has shown accuracy rates comparable to, or even higher than, traditional methods.

2. Postmortem Interval (PMI) Estimation

Estimating the Postmortem Interval (PMI), or the time elapsed since death, is one of the cornerstones of forensic investigations. Traditionally, PMI estimation is based on temperature changes, rigor mortis, or decomposition stages. However, these methods are highly variable, depending on environmental conditions.

AI has revolutionized PMI estimation by utilizing more reliable data, such as tissue degradation patterns, microbial activity, and postmortem imaging. Techniques like Fourier Transform Infrared (FTIR) spectroscopy have been developed to analyze changes in biological tissues after death, providing a more accurate estimate of PMI.

For instance, Garland et al. employed AI to process gross images of visceral organs, achieving over 95% accuracy in PMI estimation. This automated process allows forensic pathologists to determine time of death more efficiently, improving the accuracy of crime scene investigations.

3. Cause of Death (COD) Determination

The determination of the Cause of Death (COD) is crucial in any forensic investigation, but it can often be complicated by complex injuries or environmental factors. AI systems, particularly CNNs and BPNNs (Backpropagation Neural Networks), are being developed to assist in this challenging task.

For example, DeepIR, a CNN-based model, is used to analyze pulmonary edema fluid samples to predict causes of death such as drowning, asphyxiation, and sudden cardiac arrest. AI systems analyze spectrographic data to differentiate between these CODs based on subtle differences in biological markers. This not only accelerates the investigation but also minimizes human error, offering more reliable results.

AI is also being used to determine COD in cases involving trauma, such as gunshot wounds or blunt force injuries. AI-driven models can analyze postmortem imaging, such as CT scans, to identify fatal injuries and suggest probable causes of death.

AI Technologies in Forensic Sciences

The integration of AI into forensic sciences has brought various technologies into the spotlight. Each has specific applications, from image recognition to biological data analysis:

1. Convolutional Neural Networks (CNNs)

CNNs are a type of neural network primarily used for processing images. In forensic sciences, CNNs are used to analyze postmortem imaging, such as CT scans, X-rays, or MRIs. These models are adept at recognizing patterns, making them ideal for identifying injuries, fractures, or foreign objects like bullets in a body.

For instance, CNNs are used in Forensic Pathology to identify traumatic brain injuries in postmortem CT scans. In these cases, AI models can detect microfractures and internal bleeding that may go unnoticed during traditional autopsies.

2. Artificial Neural Networks (ANNs)

ANNs are computational models that mimic the human brain’s neural networks. They are used in forensic sciences for tasks that involve pattern recognition, classification, and regression. ANNs have proven to be particularly effective in analyzing bone density, skeletal remains, and other biological markers to estimate the age or sex of an individual.

In the field of forensic dentistry, ANNs are used to analyze dental records for human identification. By processing dental radiographs, ANNs can compare dental patterns against existing records to identify victims in mass disasters or criminal investigations.

3. Backpropagation Neural Networks (BPNNs)

BPNNs are a subset of ANNs used to train models through error minimization. In forensic sciences, BPNNs have been employed to determine sex from cranial remains, achieving accuracy rates as high as 96%. BPNNs are particularly useful in cases where physical features have been obliterated or decomposed, making traditional methods less effective.

4. k-Nearest Neighbor (k-NN) Algorithm

The k-NN algorithm is a simple yet effective machine learning technique used for classification and regression tasks. In forensic sciences, it is used for PMI estimation and human identification. By comparing unknown data points (such as biological samples) with known data points in a multi-dimensional space, k-NN can accurately predict categories, such as time since death or the identity of the victim.

For example, Johnson et al. employed k-NN to analyze microbiomes collected from different anatomical areas of decomposing bodies. This method allowed them to estimate PMI with a high degree of accuracy.

5. Robust Object Detection Framework (RODF)

RODFs are used in forensic sciences for object detection, especially in complex visual datasets. In cases of drowning, AI models like RetinaNet, a CNN-based detection framework, have been employed to automate the diatom test—an important forensic test to determine if a victim drowned. By analyzing microscopic images of diatoms (a type of algae), RODFs improve the speed and accuracy of the test, helping forensic pathologists reach conclusions faster.

Future Perspectives: Challenges and Opportunities

The integration of AI in forensic sciences is just beginning, but several challenges and future opportunities remain:

1. The Need for Larger Datasets

While AI has shown immense potential in forensic sciences, most AI models rely on large datasets for training. In fields like forensic pathology and genetics, gathering sufficiently large datasets can be difficult due to the sensitive nature of the information. Future advancements in AI will require collaboration between institutions to create standardized datasets that can be used across different regions and populations.

2. Expansion in Forensic Genetics

AI’s potential in forensic genetics is vast. Future AI models could help overcome current limitations in DNA analysis, such as improving PCR techniques or minimizing errors in electropherogram readings. Additionally, AI could assist in age estimation from biological samples left at crime scenes, offering critical insights when traditional DNA matching methods fail.

3. Pattern Recognition and Crime Scene Analysis

The ability of AI to recognize patterns is one of its most exciting features. In the future, AI could analyze crime scenes, identifying critical elements like bloodstain patterns, weapon marks, or victim positioning. Such technologies could assist forensic investigators by offering probable scenarios or suggesting cause of death based on physical evidence.

While these applications are promising, they also require large, reliable datasets to ensure accuracy and avoid bias. As AI continues to evolve, it will likely play a central role in crime scene reconstruction and forensic analysis.

Conclusion

The advent of Artificial Intelligence in forensic sciences marks a revolutionary shift in how forensic investigations are conducted. From postmortem interval estimation to human identification and cause of death determination, AI offers forensic pathologists powerful tools to enhance their capabilities and ensure more accurate, faster investigations.

While challenges remain, such as the need for larger datasets and improved hardware, the future of forensic sciences is undeniably intertwined with AI. As technology continues to evolve, AI will increasingly assist forensic experts, helping to unlock the mysteries behind unsolved cases and bringing justice to victims and their families.