Indian Journal of Forensic Medicine and Pathology

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Background: Facial features are known to be highly heritable, exhibiting remarkable resemblance within families across generations. This inheritance pattern has significant implications in fields such as forensics, where reconstructing facial characteristics from limited ancestral data can aid in identification and investigation. Aims: This study aims to leverage artificial intelligence (AI) and machine learning techniques to conduct a comprehensive computational analysis of craniofacial heritability within Indian families. Methods: A dataset comprising facial photographs of three generations (grandparents, parents and children) from 51 Indian families were compiled. Computer vision algorithms were employed to extract precise anthropometric measurements from these images. Various statistical methods, including Pearson correlation, hypothesis testing (T-tests, ANOVA, chi-square) and dimensionality reduction techniques (PCA, PCoA), were applied to quantify intergenerational relationships. Furthermore, machine learning models, such as linear regression and random forest regression, were developed to predict descendant facial features from ancestral data. Results: Pearson Correlation Analysis revealed exceptionally strong positive correlations (r > 0.9) between ancestral and descendant facial measurements, supported by statistically significant p-values. Hypothesis tests failed to reject the null hypothesis of no difference between generations, indicating remarkable similarity. Dimensionality reduction visualizations depicted clustering patterns that illustrated familial resemblance and generational variations. Machine learning models achieved high predictive accuracy, with random forest regression outperforming linear regression, capturing complex non-linear hereditary patterns. Conclusions: This study demonstrates the powerful capabilities of AI and machine learning techniques in quantifying and elucidating the heritability of craniofacial morphology across generations. The findings conclusively establish that facial features are highly heritable within Indian families, with genetics playing a predominant role over environmental influences. These computational forensic methods advance our ability to reconstruct facial characteristics from limited ancestral data, enhancing forensic investigations and deepening our understanding of phenotypic inheritance.

Context: The rapid advancement of deepfake technology has posed a significant challenge in the field of audio authentication, blurring the line between real and manipulated voices. Aims: This research aims to understand the intricate relationship between vocal feature parameters of authentic and deepfake voices and contribute to deepfake detection by analyzing vocal features and employing AI methodologies. Settings and Design: The study utilized the “DEEP-VOICE: DeepFake Voice Recognition” dataset and employed correlation analysis, descriptive statistics, visualization techniques and machine learning models. Methods and Material: Vocal features like duration, frequency, MFCCs and others were extracted. Correlation matrices, descriptive statistics and comparative visualizations were used to analyze differences between real and deepfake voices. Logistic Regression models were trained on the vocal features. Statistical analysis used: Correlation coefficients, means, standard deviations and machine learning model evaluation metrics were employed. Results: Features like Duration, Number of Pulses and Voice Breaks exhibited negative correlations with deepfakes, while Mean Frequency and Median Frequency showed positive correlations with real voices. Descriptive statistics and visualizations highlighted differences in pitch and spectral characteristics. Logistic Regression models achieved promising performance, with pitch-related features being most effective. Conclusions: The study identified key vocal feature parameters associated with real and deepfake voices, providing insights for developing robust voice authentication techniques and advancing deepfake detection.

Context: Epilepsy, also known as “The Falling Sickness”, is a neurological disorder that occurs due to nerve cell disruption which results in improper signal transferring leading to seizures. Occurrence of abuse of Anti-convulsant drugs (ACDs) is increasing day-by-day, so the need for detection of these drugs due to their involvement in forensic cases. Aims: In this research paper, Dispersive Liquid-Liquid Microextraction (DLLME) has been used for extraction of 4 ACDs-Lamotrigine (LMG), Oxcarbazepine (OXC), Valproic Acid (VA) and Topiramate (TPM). Settings and Design: Ultrahigh Performance Liquid Chromatography Tandem Mass Spectrometry (UHPLC-MS/MS) was employed to analyze data utilizing multiple reaction monitoring (MRM) from biological matrix encountered in forensic and clinical scenarios. Methods and Material: DLLME extraction method has been employed for isolation of 4 ACDs- LMG, OXC, VA & TPM from urine sample. Further quantification and analysis of drugs was employed by LC-MS/MS method. Statistical analysis used: MS-Excel and GraphPad Prism softwares are used to calculate the linearity between the response and 6 different concentrations of standards taken for each drug. Results: Linearity was obtained in the range of 5 – 200 µg/L for the targeted drugs. LOD and LOQ for the analytes were in the range of 7.5844 µg/ml to 12.1447 µg/ml and 22.9831 µg/ ml to 36.8022 µg/ml. This method resulted in the highest recovery of 73 to 119 % for targeted drugs as compared to other published methods. The complete process followed ICH guidelines for optimization and validation of method. Conclusions: A novel, sensitive and effective extraction method has been developed for concurrent extraction of 4 ACDs- LMG, OXC, VA & TPM from saliva sample. Values of LOD and LOQ obtained were lowest as compared to existing methods.

During crime scene inspection, footwear impressions (shoe prints) are one of the most common pieces of evidence. It can establish the facts of the absence and presence of an individual at the scene, as well as the linkage of minute traces of the soil, dust, dirt, or any such material to the crime scene and the surroundings. In this study, the right-leg shoe prints from 100 participants (59 females and 41 males) of the Haryana population within the range of 18 to 50 years were collected. The parameters selected were shoe length, shoe breadth shoe size along with manufacturing marks (if any), wear and tear pattern, etc. Based upon the statistical analysis, this study provides us with information that shoe size and shoe breadth can be considered a few important parameters to differentiate between male and female footwear impressions. Therefore, this study could be used as a method for the gender determination of an individual from the available shoe print present at a crime scene and can provide a more accurate link with the suspect.

Background: In India, 1.77 lakh people died in traffic accidents in 2015. Thoracic cage houses body’s most important organs for breathing and circulation. Any damage to it has a significant negative impact on one’s chances of living. Objective of this study is to examine pattern of abdominal and thoracic injuries in fatal motor vehicle accident cases. Study design: Autopsy cases of road traffic accidents sustaining thoraco abdominal injuries which are brought to mortuary of Belagavi Institute of Medical Sciences Civil Hospital and KLE’s Dr. Prabhakar Kore Hospital. Sample size: 105. Results: 82% victims were males and 52% were among age range of 21-40 years. Commonest cause for death was hemorrhagic shock in 51 cases (48.57%) and most common period of survival was within 2 hours of occurrence of accident in 30 victims (28.57%). The commonest organ to be involved was lungs in 67 (63.8%) cases and second most common organ and commonest abdominal organ to be involved was spleen in 55 (52.38%) cases, 3rd commonest organ and 2nd commonest abdominal organ to be involved was liver in 50 ( 47.6%) cases. The commonest injury seen was rib fracture in 60% cases and hemoperitoneum was commonest abdominal injury found in 49 (46.66%) cases followed by liver laceration in 31 cases (29.52%). Conclusion: Non Government Organizations, police and also individuals who witness any accident should provide prehospital care to injured patients which will reduce fatality to a large extent. Ambulances should not be just transport vehicles but they should be well equipped with pre hospital treatment facilities.

Context: Medical autopsies which carried out to identify the cause of death; medicolegal autopsies are performed to describe the injuries and identify the precise COD (Cause of Death) in cases involving suspicious circumstances. Aims: To study the significance of histopathological examination and pattern of disease in autopsy specimens. Settings and Design: Descriptive Study conducted at Vilasrao Deshmukh Government Medical College, Latur. Materials and Methods: This descriptive research has been performed among all specimens received during 18 months of data collection period in Department of Pathology at Vilasrao Deshmukh Government Medical College, Latur during the study period from January 2021 to December 2022. Results: Distribution according to histopathological findings in the Heart showed that normal histology was observed in 34.2% of specimens. This is followed by 18.4% had atherosclerosis. Distribution according to histopathological findings in Lung showed that 40.7% had pulmonary edema followed by 33.3% had pneumonia. Distribution according to histopathological findings in the Liver showed that 56.7% had congestion, and 25% had fatty liver. Distribution according to histopathological findings in the kidneys showed that the majority had cloudy changes i.e. 59.6% followed by 21.3% with chronic pyelonephritis. COD in the majority of the cases was sudden death i.e. 86.5% followed by 8.3% having road traffic accident, 3.1% with myocardial infarction and 2.1% had drowning. Conclusions: The most commonly received organs were the heart, lungs, and brain. Commonly observed findings in the heart were atherosclerosis (18.4%), in lungs-Pulmonary edema (40.7%), in liver-congestion (56.7%), in kidney-cloudy kidney (59.6%), brain-congestion (52.1%). Sudden death is a cause of death in almost all age groups with females preponderance for sudden deaths.

Introduction: Consumption of household chemicals like insecticides, rodenticides, repellents, detergents etc is common in cases presenting to casualty. A lot of incidents among these are caused by accident and completely preventable only if the person using them had awareness or basic understanding of the effects that the chemicals present in the products can cause. Material and methods: In this observational cross-sectional study a total of 345 participants answered the questionnaire to check awareness and attitudes of people about the use, precautions, and accidental poisoning of common household poisons based on MCQ type questionnaire. Observation & results: In this study, for 20 questions 14 questions have more than 50% correct reply; and 3 questions got more than 90% correct responses. It shows although there is some basic knowledge regarding household poisons more awareness & training programmes are needed as it is a matter of life & death. Conclusion & recommendations: The lack of proper knowledge and awareness of the potential dangers of household poisons and their increased use has caused an increase in cases of household poisoning. Everyone should have at least the basic knowledge of the potential harms that common household items can cause. Very old and very young members of the family should be kept away from household “poisons”. Making it a habit to read the labels on any new product that one buys.

Background: A medico-legal case (MLC) is characterized as one in which a medical condition or injury occurs, and the law enforcement authorities must conduct an investigation to determine who is responsible for what caused it. The goal of the current study is to determine the workload and profile of medico-legal cases and understand the various forms of MLC, the age and sex wise case distribution that was recorded at the casualty. Materials and methods: This two-year retrospective study is hospital-based and was carried out at the casualty unit of tertiary care hospital in Karaikal between June 2022 and May 2024. 319 medico-legal cases in all were registered throughout this time. Result: Among the 319 cases, 231 (72.41%) were male and 88 (27.59%) were female. The bulk of cases (N = 99; 31.03%) include victims who are between the ages of 21-30. In the present research, road traffic accidents (RTA) account for the bulk of cases (137 cases, or 52.35%). Out of the 119 occurrences, 37.3% happened between 12:01 and 6 p.m. most victims (n = 249: 78.06%) died in an accident, including motor vehicle accidents. Conclusion: MLC increased significantly in the years 2022–2023 and 2023–2024. Implementing road traffic laws strictly, abstaining from alcohol and drugs, wearing seat belts and helmets, refraining from using cell phones while driving and not speeding are all ways to lower the number of road traffic accidents.

Background: Autopsies are critical in determining the cause and manner of death, providing valuable insights into mortality patterns, and forming public health strategies. Aims & Objectives: This study aims to determine the socio-demographic profile of the autopsy cases and compare the different autopsy cases conducted in a tertiary care hospital in rural Haryana to identify prevalent causes of death and highlight areas for public health intervention. Methodology: This is a prospective observational study include 200 autopsy cases conducted in the mortuary at a tertiary care hospital in Ambala, Haryana from March 1, 2023, to April 30, 2024. Data were collected from the postmortem examination findings and police inquests, and descriptive statistics were analyzed. Results: Of the 200 autopsy cases, majority, 170 (85%) were males. Most of the autopsies, 103 (51.5%) cases, were in the 21-40 years age group, followed by 54 (27%) cases in 41-60 years. Urban domicile constituted 125 (62.5%) of cases, indicating a higher prevalence of mortality events among individuals residing in urban areas. The majority 163 (81.5%) cases, were individuals of the Hindu faith. The most common cause of death was due to RTAs (105 cases, 52.5%), which resulted in head injuries, multiple mechanical injuries, hemorrhagic shock, etc., followed by poisoning, which constituted 53 (26.5%) of cases. Most of the autopsy cases were of accidental deaths, 120 (60.0%), followed by suicide, 65 (32.5%). Conclusion: The study highlights the urgent need for targeted public health interventions to address the high incidence of RTAs and poisoning in Haryana.

Cases of carbon monoxide (CO) poisoning are challenging for toxicology laboratories, on the other hand immediate confirmation of CO poisoning is sometimes very essential for investigating agencies. Though autopsy findings are self-sufficient for this confirmation, a laboratory validation adds more weightage. Currently, in many of our Regional Forensic Science Laboratories (RFSL), spectrophotometric analysis is used for the detection of Carboxy-haemoglobin (COHb). However, samples being susceptibile to alterations of optical state and degradation during sampling, transport and storage pose difficulties in analysis. Secondly reports from RFSL are received after much longer durations. However, hospitalbased blood gas analyzers with CO-oximetry, provide automated spectrophotometric measurement of concentration of total haemoglobin (ctHb) in blood along with that of other four haemoglobin derivatives and COHb levels, that too immediately and at a low cost. Over the background of delayed reporting from RFSL, we hereby discuss five cases of CO poisoning, confirmed with the help of hospital-based blood gas analyser.