Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis : The Application of an Artificial Intelligence Based Program to Classify Oral Cavity Findings Based on Clinical Image Analysis
Early diagnosis of oral lesions, particularly oral cancer, is crucial for enhancing prognosis, facilitating early intervention and care with the intention of lowering disease-related mortality.Since conventional oral examination (COE) is the most used method in identifying oral lesions, the average dental practitioner's experience is a decisive factor in early diagnosis.Visual examination lacks specificity and sensitivity since its highly subjective. Unfortunately, Studies show that the majority of dentists lack expertise in early detection of the disease, resulting in false negative diagnosis of oral lesions.General practitioners are found to either delay the referral of a suspected oral lesion to an Oral Medicine specialist, or referring numerous false positive cases, unnecessarily pushing the patients into a state of anxiousness and cancer phobia. False positive referrals overburden the specialists, which will eventually cause delayed diagnosis of true positive cases due to the oversaturation with false positive ones.diagnostic research scope shifts towards noninvasive, easy chair side methods with higher accuracy for early detection of oral lesions. Recent approaches towards using machine based programs indicate that this machine-learning method may be useful in the detection and diagnosis of oral cancer..
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Klinische Studie| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
ClinicalTrials.gov - (2024) vom: 26. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
610 |
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| Anmerkungen: |
Source: Link to the current ClinicalTrials.gov record., First posted: March 22, 2024, Last downloaded: ClinicalTrials.gov processed this data on April 03, 2024, Last updated: April 03, 2024 |
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| PPN (Katalog-ID): |
CTG000140147 |
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| 520 | |a Early diagnosis of oral lesions, particularly oral cancer, is crucial for enhancing prognosis, facilitating early intervention and care with the intention of lowering disease-related mortality.Since conventional oral examination (COE) is the most used method in identifying oral lesions, the average dental practitioner's experience is a decisive factor in early diagnosis.Visual examination lacks specificity and sensitivity since its highly subjective. Unfortunately, Studies show that the majority of dentists lack expertise in early detection of the disease, resulting in false negative diagnosis of oral lesions.General practitioners are found to either delay the referral of a suspected oral lesion to an Oral Medicine specialist, or referring numerous false positive cases, unnecessarily pushing the patients into a state of anxiousness and cancer phobia. False positive referrals overburden the specialists, which will eventually cause delayed diagnosis of true positive cases due to the oversaturation with false positive ones.diagnostic research scope shifts towards noninvasive, easy chair side methods with higher accuracy for early detection of oral lesions. Recent approaches towards using machine based programs indicate that this machine-learning method may be useful in the detection and diagnosis of oral cancer. | ||
| 650 | 2 | |a Mouth Neoplasms | |
| 650 | 2 | |a Leukoplakia | |
| 650 | 2 | |a Lichen Planus, Oral | |
| 650 | 2 | |a Leukoedema, Oral | |
| 650 | 2 | |a Lichen Planus | |
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