Introduction
Artificial intelligence is used widely in tracing disease patterns and aids in the diagnosis and prediction of post-operative treatment outcomes after dental, oral, or reconstructive face surgery. This article will explore all the current applications of AI in dentistry.
What Is the Advent of Artificial Intelligence in Dentistry and Oral Surgery?
The world of artificial intelligence has already penetrated dentistry, modern-day maxillofacial surgery, and periodontal therapy. The field of artificial intelligence (AI) has experienced, in fact, a tremendous rate of global development and has progressed in dentistry as well, in terms of application over the past two decades. With the most recent progress being that of digitized data acquisition, current concepts in dentistry can now infuse the AI basics of machine learning and computing infrastructure into the areas of dental surgery, dental implantology, periodontics, orthodontics, and oral surgery.
Hitherto, these AI applications were thought to be primarily reserved only for a few human experts who had access to the implementation in medical and dental machine designing. However, recent advances in implant dentistry over the last decade in the form of computer-guided surgical templates and computer-guided implant surgery, as well as computer-controlled local anesthetic delivery (CCLAD) are all the modern-day revolutionizing concepts with known benefits like a decline in postoperative complications observed by dental research, less room for dental operator based errors, increasing the quality of prosthetic life, improving operators decision-making and finally in decreasing the number of unnecessary surgical procedures, manually that may otherwise be creating a natural surgical and post-operative trauma, in dental cases.
What Are the Two Principles of AI?
1. Machine Learning and Deep Learning:
AI has been used extensively in dental laboratories, with its main application in current dentistry based on a branch of computer science that builds both entities and software programs for the field. AI would be defined in literature as the sequence of operations that are designed or specifically meant for performing a task, allowing less scope for manual operation. For example, a medical system that is designed to detect lesions in radiographic imaging utilizes the same basic principles of AI- machine learning and deep learning in detecting the pathologies and in sharing the exact location of pathology with the operator. Modern-day CBCT-cone beam computed tomography is one of the best examples that can help radiographically detect oral and maxillofacial lesions, even potentially aiding in the detection of soft tissue cancers as well.
Machine learning (ML) is the branch of AI that's integrated into medical, dental imaging, radiographic, or surgical devices, in which the algorithm identifies patterns that can be applied to new images.
Deep learning (DL) on the other hand is the branch of AI that utilizes the interbuilt hierarchy of composable patterns or that can help detect or diagnose hidden layers or assist in deep imaging. The most useful aspect of AI in dentistry is because of the convolutional neural network (CNN/ConvNet) which are the classes of deep neural networks, commonly applied in order to get the best visual results or in analyzing visual imagery in dentistry.
2. Integrating CNNS Into Radiographic Imaging:
CNNs hold promising potency in both detecting and identifying oral anatomical structures and their exact locations in 3-dimensional views. Moving on from the traditional 2D periapical imaging, CNNs have demonstrated a precision rate in modern 3D radiographic imaging like CT, with almost 95.8 to 99.45 percent in diagnosing, detecting, and identifying oral or maxillofacial lesions. In oral surgeries, especially as a part of pre-operative evaluation by the dentist and oral and maxillofacial surgeon- these radiographic modalities hold great scope for achieving predictable outcomes for dental patients. Say, in the case of assessing a patient's orthodontic tooth plan or visualizing how a dental implant can be accurately placed preoperatively or in the design of surgical templates, clinical decisions can be best made using the CNNs in the radiographic software.
Further modern imaging like CBCT (cone-beam computed tomography), multi-detector CT scanning (MDCT), DEXA (dual phase energy absorptiometry), etc can all be useful in the assessment or evaluation of a patient's bone density and also for accurate surgical placement of dental implants, resection, or reconstructive surgery modalities, etc as a part of the preoperative evaluation by the operator itself.
Also, because of the complex pathogenesis that is usually associated with most dental diseases. AI has been a great way to assess the clinical, microbiological, histopathological as well as genetic causes or variations of the disease. Although this is a futuristic field with great scope further in diagnosing and detecting dental diseases, they are already being used in conventional dentistry as well.
For instance, take the CBCT example elaborated above. Even though it is a gold-standard radiographic modality, it can have a higher radiation dose compared to that the patients may be receiving via conventional periapical 2D (two-dimensional) radiographs or panoramic or occlusal radiographs. To overcome such drawbacks in CBCT, modern-day machines are now being integrated with CNN software that utilizes the machine learning and deep learning aspects of AI technology.
Not only can the radiographic modalities now determine the presence of an extra tooth root, for example, but they can also give you the dimensions, the exact number of tooth roots, the location of any pathology with respect to it, and also any atypia associated with the tooth in question. This can prove particularly beneficial in the examination of teeth indicated for endodontic and surgical treatments.
What Is the Role of CNNs in Periodontal Treatment and Surgery?
Similarly, CNNs have been used to detect and discriminate between chronic and aggressive periodontitis patients. CNNs can help the dentist or periodontists detect the immunologic parameters such as interleukins, oral antibody titers of IgG, and leukocytes for accurately differentiating between periodontal conditions.
In detecting oral cancers, as aforementioned- integration of CNNs into dental, oral, or head and neck radiographic imaging can prove valuable to detect the location and exactly assess the tumoral qualities in cancer lesions.
Conclusion
Artificial intelligence (AI) is one of the fast-moving technologies that can hence enable machines to perform tasks primarily designed to reduce patient's chair time with the dentist or operating surgeon. The use of AI is currently underway in most aspects of dentistry, however, soon- CNNs, and convolutional neural networks hold the promising scope to assist dentists and surgeons or specialists in correct, informed, and crucial decisions in dental or oral cases.
