Artificial intelligence application for identifying toxic plant species: A case of poisoning with Datura stramonium
| dc.contributor.author | Kokulu, Kamil | |
| dc.contributor.author | Sert, Ekrem Taha | |
| dc.date.accessioned | 2024-11-13T08:06:16Z | |
| dc.date.available | 2024-11-13T08:06:16Z | |
| dc.date.issued | 2024 | |
| dc.department | Tıp Fakültesi | |
| dc.description.abstract | 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. | |
| dc.identifier.doi | 10.1016/j.toxicon.2024.108129 | |
| dc.identifier.issue | - | en_US |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https:/dx.doi.org/10.1016/j.toxicon.2024.108129 | |
| dc.identifier.uri | 00410101 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12451/12617 | |
| dc.identifier.volume | 251 | en_US |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.indekslendigikaynak | PubMed | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Ltd | |
| dc.relation.ispartof | Toxicon | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/embargoedAccess | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Emergency | |
| dc.subject | Google Gemini | |
| dc.subject | Plant | |
| dc.subject | Poisoning | |
| dc.title | Artificial intelligence application for identifying toxic plant species: A case of poisoning with Datura stramonium | |
| dc.type | Article |
