AI – SaudiDent

Artificial intelligence in the detection and classification of dental caries | Summary!

Mahmoud H. Al-Johani — Sun, 03 Sep 2023 12:27:03 +0000

Artificial intelligence in the detection and classification of dental caries

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“Dental caries is one of the most common infectious chronic diseases, affecting half of the world’s population. The accurate and early diagnosis of dental caries is fundamental to determining the most appropriate treatment. Dental caries is commonly diagnosed clinically by visual-tactile assessment and radiographic examinations. Additional devices that have been used to detect dental caries include digital imaging, light-emitting diode technology, fiber-optic transillumination, and fluorescence cameras and lasers”

In a study conducted by Ahmed et al., published in The Journal of Prosthetic Dentistry (Online Version), and titled ‘Artificial intelligence in the detection and classification of dental caries’, researchers utilized advanced technology to improve the detection of dental caries, or cavities, in patients. They collected and analyzed bitewing radiographs. These radiographs were processed using a software program that segmented and anonymized the images for their analysis. The researchers employed supervised learning algorithms trained on segmentation tasks to identify and classify carious lesions based on the modified King Abdulaziz University classification. They utilized popular deep learning models – ResNet50, ResNext101, and Vgg19 – as encoders, all pretrained on ImageNet weights. Through the combination of multiple models via ensemble learning, they aimed to create a more robust and accurate caries detection model.

The evaluation of the model’s performance was based on two main metrics: the mean score for intersection over union (IoU) and the F1 score. The IoU measures the similarity between the predicted and ground truth areas, while the F1 score combines precision and recall into a single metric. These measurements were used instead of accuracy due to imbalances observed in datasets.

Results from the study showed promising outcomes. The model achieved a mean IoU score of 0.55 proximal carious lesions on a 5-category segmentation assignment and an F1 score of 0.535 using 554 training samples. Additionally, the segmentation model displayed a sensitivity of 0.76, precision of 0.87, and an F1 score of 0.81.

Comparison tests revealed that the AI models outperformed an assistant professor of dentomaxillofacial radiology with 2 years of experience and an assistant professor of restorative dentistry with 3 years of experience when assessing F1 scores.

However, the study acknowledged certain limitations. In cases where radiographs were overexposed or underexposed, the model tended to mislabel these areas as artifacts. Additionally, overlapping proximal surfaces presented challenges for accurate labeling. The size and information limitations of the dataset’s lowest truth labels also posed constraints on the model’s performance. To enhance early detection of dental caries, it would be necessary to collect a larger and more diverse set of training samples. Overall, this study validates the potential of developing an accurate car detection model that can expedite caries identification, improve clinician decision-making, and enhance the quality of patient care. With further research and improvements, this technology holds great promise for enhancing dental diagnostics and promoting early intervention in oral health.

Revolutionizing Dentistry with Artificial Intelligence: Top Uses and Challenges

Mahmoud H. Al-Johani — Sun, 03 Sep 2023 11:36:13 +0000

Revolutionizing Dentistry with Artificial Intelligence: Top Uses and Challenges

Artificial Intelligence (AI) has taken the dental industry by storm, transforming the way dental professionals diagnose, treat, and care for their patients. With its remarkable capabilities, AI is paving the way for more accurate diagnoses, personalized treatment plans, and improved patient experiences. However, along with the incredible potential come several challenges that must be navigated to unlock AI’s full potential in dentistry. In this article, we explore the top uses of AI in dentistry and the hurdles that need to be overcome. From image analysis and predictive analytics to robot-assisted procedures and virtual assistants, discover the cutting-edge applications of AI in dentistry that are reshaping the future of oral healthcare.

Some of the top uses of AI in dentistry include:

Diagnosis and treatment planning: AI algorithms can analyze dental images, such as X-rays, CT scans, and intraoral scans, to detect and diagnose oral conditions like cavities, periodontal disease, and oral cancers. AI can also assist in treatment planning by suggesting appropriate treatment options based on the patient’s condition and medical history.
Dental image analysis: AI can analyze dental images and identify anatomical structures, dental abnormalities, and dental restorations with high accuracy. This helps dentists in gaining a better understanding of the patient’s dental condition, enabling them to provide accurate treatment recommendations.
Virtual consultations: AI-powered virtual consultation platforms allow dentists to remotely communicate with patients, evaluate their dental concerns, and provide initial diagnoses. This technology can save time and improve access to dental care, especially in remote or underserved areas.
Robot-assisted dentistry: Robots equipped with AI algorithms can perform certain dental procedures with precision and consistent accuracy. They can assist with tasks like tooth preparation, dental implant placement, and even teeth cleaning, reducing human error and improving treatment outcomes.
Predictive analytics and risk assessment: AI can analyze patient data, such as medical history, lifestyle habits, and dental records, to assess the risk of developing oral diseases. This helps dentists in providing personalized preventive care recommendations and early intervention strategies based on the individual patient’s risk profile.
Speech recognition and natural language processing: AI-powered voice recognition technology can transcribe and analyze conversations between dentists and patients, helping to generate accurate dental records and treatment plans. Natural language processing algorithms can also assist in clinical documentation and automate administrative tasks.
Patient monitoring and personalized care: AI can monitor patient data in real-time, such as vital signs, sleep patterns, and oral hygiene habits, to provide personalized recommendations and interventions for maintaining oral health. This helps patients in proactive dental care management and preventive strategies.

However, there are also several challenges in implementing AI in dentistry:

Data quality and quantity: Access to sufficient high-quality dental datasets can be a challenge, as it requires large and diverse data sets to train accurate AI models.
Privacy and security: Protecting patient data and ensuring compliance with relevant regulations like HIPAA (in the United States) while using AI algorithms is crucial to maintain patient confidentiality.
Regulatory considerations: AI software and algorithms used in dentistry may need to comply with specific regulations and receive appropriate approvals from regulatory bodies.
Integration and interoperability: Integrating AI systems with existing dental software and equipment can be challenging, as it requires seamless data exchange and compatibility.
Ethical considerations: Addressing ethical concerns around AI decision-making, patient consent, and potential biases in the algorithms is important to ensure responsible and unbiased AI usage in dentistry.

Overcoming these challenges is crucial for the successful integration of AI in dentistry and reaping its benefits while ensuring patient safety and ethical practices.

Advanced Oral Lesion Classification and Diagnosis Using Deep Neural Networks

Mahmoud H. Al-Johani — Tue, 29 Aug 2023 09:59:12 +0000

The introduction highlights the global impact of oral cancer and the alarming increase in mortality rates over time. While survival rates vary, early diagnosis is crucial for improving outcomes. The challenge of late-stage diagnoses, especially in remote areas, underscores the need for a simple, low-cost diagnostic tool. Oral squamous cell carcinoma (OSCC) is the predominant form of oral cancer, often originating from potentially malignant disorders (OPMDs). Detecting OPMDs early is essential, as some can progress to malignancy. Conventional visual assessment forms the basis of many screening programs, but it has limitations.
Artificial Intelligence (AI) has emerged as a potential solution for lesion detection. AI, modeled after human thought processes, shows promise despite its current limitations. Deep Convolutional Neural Network (DCNN) models have successfully detected precancerous skin lesions, achieving high accuracy. Some studies have used AI-based systems with pre-collected images, addressing limited access to care. The goal is to create an AI system that can be operated by frontline healthcare workers, including those without formal training, and can connect with remote specialists for informed diagnoses. This approach is expected to improve screening by distinguishing potentially malignant disorders from benign lesions, enabling early treatment.
The challenge lies in the computational demands of DCNN models, particularly for smartphone applications. Google’s efforts have led to the development of efficient models like MobileNet and EfficientNet, designed to balance accuracy and efficiency. Despite limited research in the field, this study aims to assess the accuracy of DCNN models, specifically EfficientNetV2 and MobileNetV3, in detecting and distinguishing precancerous oral lesions using pre-collected images. The hypothesis is that these models can accurately categorize oral lesions into different classes, aiding in early diagnosis and treatment.
The study found that EfficientNetV2 and MobileNetV3 achieved accurate identification (82-84%) of lesions in Classes 2 and 3, while accuracy was lower for Class 1 lesions (64-63%). This discrepancy suggests the models might excel at detecting severe lesions, highlighting a need to improve identification of milder or non-neoplastic lesions. Additional analyses using confusion matrices and ROC curves confirmed varying accuracy levels across lesion classes, with promising performance indicated by high AUC values of 0.88.
Prior research explored AI and computer vision for oral cancer diagnosis, demonstrating the feasibility of photographic imaging in assessing malignant disorders. Other studies revealed the potential of Convolutional Neural Networks (CNNs), achieving high AUC values. This proof-of-concept study demonstrated the feasibility of Deep Convolutional Neural Networks (MobileNetV3 and EfficientNetV2) for pre-collected oral lesion classification. While results are promising, further model development, especially for non-neoplastic lesions, is necessary.
The study’s outcomes could lead to a smartphone app for automated premalignant oral lesion diagnosis, utilizing portable image collection, computation, and data transmission. Challenges include accurate lesion identification within a focused field of view, and the need for further research to consolidate findings for clinical application.
The study acknowledges limitations, such as models struggling with certain lesion classes, not utilizing textural filters to assess baseline model performance, and lacking differentiation between lesion subtypes. Future work could address these limitations and enhance the understanding of model performance.
The study highlights the capacity of AI to improve remote oral lesion screening, aiding underserved populations. The proof-of-concept study effectively showcased the potential of AI, specifically MobileNetV3 and EfficientNetV2, to classify and recognize oral lesions. While the models showed promise in distinguishing between certain lesion types, their accuracy in detecting non-neoplastic lesions requires further improvement.

▪️- Further reading:
Malignant and non-malignant oral lesions classification and diagnosis with deep neural networks
https://www.sciencedirect.com/science/article/pii/S0300571223002439

Ethical Integration of Artificial Intelligence in Dental Education

Mahmoud H. Al-Johani — Thu, 03 Aug 2023 14:37:41 +0000

▪️ Introduction:

AI is increasingly being used in healthcare and dental education must prepare students to use it responsibly.
AI should support clinician decision-making, not replace it. Algorithms must be unbiased and clinically validated.
Dental curriculum must teach critical thinking skills and ethical application of AI, including limitations and potential biases.

▪️ Considerations when adopting AI:

AI is a tool, not a replacement for clinician knowledge and expertise.
AI algorithms must account for patient factors like race, gender, culture to avoid unintended consequences.
Academic integrity issues must be addressed regarding proper use and citation of AI.

▪️ Proposed curriculum model:

Introduce AI incrementally throughout program with multidisciplinary perspectives.
Preclinical: Explore datasets, research opportunities, interest groups.
Clinical: Case studies on ethics, legal issues, principles for selection of AI tools.

▪️ Guidance from organizations:

ADEA should provide resources and develop guidelines for AI curriculum.
Collaboration between ADEA and ADA is important.
Continuing education on AI should be required.

▪️ Conclusion:

AI is a rapidly advancing reality across health care. While it offers the promise of a higher quality of care and easy accessibility to information, dental education has a responsibility to be proactive and visionary in integrating AI safely and ethically for the benefit of both students and patients and future providers.

▪️ ▪️ Content summarized from:
Artificial intelligence (A.I.) in dental curricula: Ethics and responsible integration
https://onlinelibrary.wiley.com/doi/10.1002/jdd.13337

AI – SaudiDent

Artificial intelligence in the detection and classification of dental caries | Summary!

Artificial intelligence in the detection and classification of dental caries

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Revolutionizing Dentistry with Artificial Intelligence: Top Uses and Challenges

Revolutionizing Dentistry with Artificial Intelligence: Top Uses and Challenges

Advanced Oral Lesion Classification and Diagnosis Using Deep Neural Networks

Ethical Integration of Artificial Intelligence in Dental Education

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