Artificial intelligence application for identifying toxic plant species: A case of poisoning with Datura stramonium.

Toxicon

Department of Emergency Medicine, Aksaray Training and Research Hospital, Aksaray, Turkey; Department of Emergency Medicine, Aksaray University School of Medicine, Aksaray, Turkey.

Published: November 2024

AI Article Synopsis

  • Managing plant poisonings in the emergency department is complicated, mainly due to the difficulty in identifying the specific toxic plant species involved, which is critical for determining the appropriate treatment.
  • A case of a 23-year-old male who experienced severe symptoms after consuming herbal tea led to the identification of Datura stramonium (jimson weed) using AI software, as timely access to botanical expertise was unavailable.
  • The case suggests that AI tools with visual recognition can aid healthcare providers in accurately identifying toxic plants, enhancing patient safety and treatment effectiveness.

Article Abstract

Introduction: The management of plant poisonings in the emergency department (ED) presents various challenges. Foremost among these is the identification of the specific botanical species responsible for the toxic effect. In cases of plant poisoning, it is crucial to accurately identify the plant in order to promptly evaluate if it has cardiotoxic, neurotoxic, hepatotoxic, or anticholinergic properties. Furthermore, it is typically not possible to determine the identity of these plants through blood tests conducted in the ED.

Case Report: An otherwise healthy 23-year-old male patient presented to the ED with symptoms of restlessness, altered mental state, and hallucinations that occurred 2 h after consuming herbal tea. On physical examination, he was tachypneic, tachycardic, and disoriented. The pupils were bilaterally mydriatic. The patient's symptoms were consistent with both sympathomimetic and anticholinergic (antimuscarinic) toxidromes. We were unable to promptly reach a botanist to identify the plant to which the patient had been exposed. Therefore, we employed Google Gemini, an artificial intelligence software, to ascertain the plant's identity. Google Gemini identified the plant we photographed as Datura stramonium, commonly known as jimson weed, which is known to cause anticholinergic toxicity. The botanist we contacted later confirmed that the plant was D. stramonium. The patient's symptoms were alleviated with the use of intravenous diazepam and physostigmine.

Conclusion: We propose that the utilization of artificial intelligence applications with visual recognition capabilities could be beneficial for physicians, patients, and foragers of edible wild plants to accurately identify plants and distinguish toxic species.

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Source
http://dx.doi.org/10.1016/j.toxicon.2024.108129DOI Listing

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