Home Dental Radiology Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence

Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence

by adminjay

As we mentioned above, panoramic dental radiography is one of the most widely and frequently used imaging techniques in dentistry. It is safer than other imaging tools and quickly takes a wide range of dental structures so it is essential to make diagnoses and further treatment plans for patients, but due to the complicated structure of the dental region and lack of time, clinicians mainly focused on small parts of the images. If that neglected information is fully detected and noted to clinicians, it will improve the overall quality and consequences of the treatment. To achieve that goal, we applied an artificial intelligence technique which is widely used in the image analysis field to automatically detect regions of anomalies. We trained our model focused to increase the specificity to help clinicians filter out healthy panoramic dental radiography so as to decrease the number of images to be examined and to alleviate the burden of clinicians. First, we selected 17 major dental anomalies which are closely related to patients’ oral health. These anomalies can lead to serious outcomes if ignored or are related to other systemic diseases. It means that the early detection of those anomalies can prevent severe outcomes and can be used as a marker to suspect other systemic diseases. We categorized anomalies into four groups corresponding to their clinical features and locations.

First, carious lesions are considered the most prevalent problem in dentistry14. Because carious lesions cause more serious problems if ignored, prevention and early diagnosis are very important.

Many previous pieces of research applied artificial intelligence techniques to detect carious lesions13. Depending on its progress and location, various treatments can be used to treat carious lesions.

Here we divided carious lesions into four categories corresponding to their clinical features for fine-grained diagnoses; dental caries, cervical caries, proximal caries, and secondary caries. These subtypes of carious lesions show unique features which are related to their diagnoses and treatment. First, cervical caries is considered the most dangerous type because it leads to the rapid loss of tooth due to its location. Proximal caries is a type of carious lesion which is located on the surfaces between adjacent teeth. They are the most difficult type to detect because they cannot be visually or manually detected. Finally, secondary caries is a disease that occurs on the tooth after the filling. Because it takes a lot of burdens to detect21. This fine-grained diagnosis of carious lesions is important to early detection of caries before their progression to severe stages and to prevent further loss of dental tissues.

The second category of our fine-grained model, calcifications, occurs when calcium accumulates in body tissue. The diagnostic criteria of calcifications are their anatomical locations, distributions, numbers, sizes, and shapes22. Calcifications in maxillofacial areas can be found through examinations of panoramic dental radiography but there are very few studies conducted regarding them23. Though the presence of calcifications on panoramic dental radiographs is uncommon, their detection is important to prevent the further progression of diseases. We selected four calcification anomalies; Calcified carotid atherosclerosis plaque, lymph node calcifications, calcifications of the stylohyoid ligament, tonsillar calcifications (tonsilloliths)24.

Our third category is dental anomalies. We included dental disease features and abnormal structures shown in the dental region18 to this category. Dental anomalies are abnormal forms or structures of teeth in the dental area. We selected six dental anomalies which are critical factors of dental health; external root absorption, impacted tooth, periapical radiolucency, residual root, supernumerary tooth. Some of these anomalies often cause symptoms such as pain, halitosis, and bleeding, and can be used as diagnostic markers, and anatomical factors when planning further dental surgeries. For example, Periapical radiolucency is the radiographic changes around the apex of the tooth and is the sign of inflammatory bone lesions. Recent studies present that periapical radiolucency may be caused by several diseases such as cirrhosis25. External root resorption is an undesirable dental injury that causes a loss of some parts of a tooth and can be seen radiographically. This type of anomaly damages the underlying tissues and causes a number of complications including infection, loss of teeth, pain, and so on26. The positional relationship between the mandibular canal and corresponding tooth is a key anatomic factor to make surgical plans such as extraction of the mandibular third molar because damage to the inferior alveolar nerve affects the function of the stomatognathic system and the quality of life of patients27. Panoramic dental radiography is one way to evaluate the risk of nerve injury before the extraction28. Impacted teeth can cause several symptoms such as swollen gums, halitosis, and pain when opening the mouth. If ignored, it causes severe complications such as infection, cysts, absorption, and many gum diseases. A recent study presented that an impacted tooth might have some association with a large central osteoma29. A residual root is a leftover of a tooth in the jaw after an extraction. It sometimes causes infections and pain. Usually, it is recommended to extract with a local anesthetic. Finally, supernumerary teeth may lead to many severe problems like displacement, crowding, root resorption, dilaceration, loss of vitality of adjacent teeth, and even ameloblastomas and odontomas in severe cases. So, clinicians should aware of the existence of the occurrence so that they can formulate treatment plans30.

The last category is anomalies located in surrounding regions of the dental area. These anomalies are rarely related to oral health but may be used as potential markers to diagnose other related diseases. These radiographic anomalies are signs of inflammatory processes of that region and are known to be related to several diseases. For example, previous studies showed that retention pseudocysts of the maxillary sinus may have some associations with allergic and inflammatory processes, trauma, periapical and periodontal infections31, radiopacity in jaws with many osteoblastic and osteoclastic activities32, mucosal thickening of the maxillary sinus with apical periodontitis, alveolar bone loss, and so on33.

To train a deep learning model to detect these many types of anomalies, it is essential to accumulate datasets including enough number of objects for every class. In fact, the most important factor of using artificial intelligence techniques is the quality and quantity of data. We built the system to collect panoramic dental images directly from local dental clinics and manually labeled them by a dental radiography expert. For a year, we accumulated a large and high-quality dataset compared to previous studies. This dataset is also still growing, so it has great potential in this field. Our tool successfully detects given anomalies with high performance especially for specificity and demonstrates that artificial intelligence can reduce the burden of dental clinicians by reducing the number of images that should be examined manually through detecting potential anomalies and filtering normal images.

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