Use of Artificial Neural Network in Differentiation of Subgroups of Temporomandibular Internal Derangements: A Preliminary Study

Use of an Artificial Neural Network for Diagnosis of Facial Pain Syndromes: An Update

“…Many diverse applications in medicine have been developed and recent medical applications include carpal tunnel syndrome (based on history) [21], predicting recurrence of kidney stones [22], predicting symptomatic cerebral vasospasm after aneurysmal subarachnoid hemorrhage [23], identification of critical factors in patient falls [24], medical disease estimation [25], thrombotic formation in mechanical heart valves [26], decision making for fine- needle aspiration diagnosis of thyroid malignancy [27], transmandibular joint internal derangements [28], and endoscopic ultrasound in the diagnosis of pancreatic masses [29]. …”

The Reliability of Artificial Neural Network in Locating Minor Apical Foramen: A Cadaver Study

“…This network must first be "trained" by having it process a large number of input patterns and showing it what output resulted from each input pattern. Once trained, the network is able to recognize similarities when presented with a new input pattern, resulting in a predicted output pattern (27). In the current study we used a prototype that trained in a previous study (28) and the Otsu feature as a built-in.…”

“…ANNs have also been established as promising alternatives to statistical discriminate analysis because they can synthesize a considerable amount of information (or variables) without requiring statistical modeling of the problem (25). Because medical diagnosis is one of typical pattern recognition problem, previous applications have inspired the use of ANNs in the dentistry field such as cephalometric diagnosis (26) and differentiation of subgroups of temporomandibular internal derangements (27).…”

Artificial intelligence: A dentist's perspective

“…Hence, when trained ANNs were tested and compared with the diagnosis of a surgeon, the results revealed high sensitivity and specificity of ANN, thereby insisting on the importance of AI in achieving correct interpretations and reducing human errors. [3] The neural network may be of value for the identification of individuals with a high risk of oral cancer or precancer for further clinical examination or health education. [5] This could quench the ever-existing need to devise a method for the early detection of oral cancer which accounts for 30% of all the cancers in India.…”

“…The various techniques of AI which are being applied in dentistry include artificial neural networks (ANN), genetic algorithms (GA), and fuzzy logic. Following its inception in 1959 when the first computational trainable neural networks were developed, [3] the field of medicine and dentistry has witnessed innumerable research using AI.…”