Combining Digital Camera Metadata and Artificial Intelligence to Sort Postmortem Gross Photographs: A Novel Approach in Image Classification in Forensic Pathology.

Authors

Jack Garland, Katarina Needham, Benjamin Ondruschka, Ugo Da Broi, Kilak Kesha, Rexson Tse

Published in

The American journal of forensic medicine and pathology. Aug 21, 2026. Epub Aug 21, 2026.

Abstract

Reviewing postmortem photographs is routine in forensic pathology. Although standard photographic techniques are used, sorting postmortem photographs can be overly time-consuming, especially when reviewing large quantities of photographs. Artificial intelligence may assist in identifying body parts/locations but is challenged in determining spatial relationships such as sidedness, angle, and distance/depth. Metadata stored in modern digital cameras provides physical data that would assist in determining this information, which can be critical in the legal setting. This proof-of-concept study used photograph metadata and artificial intelligence to develop a tool to sort postmortem photographs using living volunteer subjects. This "tool" automatically sorts through general, close-up images (hands, eyes, face, and hands) and mock injury (with a scale) photographs. The study compared using photograph metadata and artificial intelligence in different proportions (hybrid photograph metadata and artificial intelligence vs. artificial intelligence alone). Results demonstrated that using both metadata and artificial intelligence for different tasks was more accurate and efficient than using artificial intelligence alone.

PMID:
42625270
Bibliographic data and abstract were imported from PubMed on 21 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.