Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions

The Use of Artificial Intelligence in Endodontics

Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning.

Autologous Transplantation Tooth Guide Design Based on Deep Learning

BACKGROUND

Autologous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient cone-beam computed tomography (CBCT) scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated.

PURPOSE

We aimed to assess the feasibility of replacing the traditional design pipeline with a deep learning-enabled autologous tooth transplantation guide design pipeline.

STUDY DESIGN, SETTING, SAMPLE

This retrospective cross-sectional study used 79 CBCT images collected at the Guangzhou Medical University Hospital between October 2022 and March 2023. Following preprocessing, a total of 5,070 region of interest images were extracted from 79 CBCT images.

PREDICTOR VARIABLE

Autologous tooth transplantation guide design pipelines, either based on traditional manual design or deep learning-based design.

MAIN OUTCOME VARIABLE

The main outcome variable was the error between the reconstructed model and the gold standard benchmark. We used the third molar extracted clinically as the gold standard and leveraged it as the benchmark for evaluating our reconstructed models from different design pipelines. Both trueness and accuracy were used to evaluate this error. Trueness was assessed using the root mean square (RMS), and accuracy was measured using the standard deviation. The secondary outcome variable was the pipeline efficiency, assessed based on the time cost. Time cost refers to the amount of time required to acquire the third molar model using the pipeline.

ANALYSES

Data were analyzed using the Kruskal-Wallis test. Statistical significance was set at P < .05.

RESULTS

In the surface matching comparison for different reconstructed models, the deep learning group achieved the lowest RMS value (0.335 ± 0.066 mm). There were no significant differences in RMS values between manual design by a senior doctor and deep learning-based design (P = .688), and the standard deviation values did not differ among the 3 groups (P = .103). The deep learning-based design pipeline (0.017 ± 0.001 minutes) provided a faster assessment compared to the manual design pipeline by both senior (19.676 ± 2.386 minutes) and junior doctors (30.613 ± 6.571 minutes) (P < .001).

CONCLUSIONS AND RELEVANCE

The deep learning-based automatic pipeline exhibited similar performance in surgical guide design for autogenous tooth transplantation compared to manual design by senior doctors, and it minimized time costs.

Practical Applications of Artificial Intelligence in Spine Imaging: A Review

Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.

CADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence

Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.