If a dataset is available we can do it. However I am yet to see a dataset. If you have a dataset do share I can get it working in some time.

Yes with a proper dataset, it surely is possible. The dataset but should consist of as many underlying extraction cases as possible for it to able to analyze properly. And with a higher amount of data, some SOTA DL methods would definitely yield very good results.

Yes, orthodontic extraction patterns can be predicted using machine learning, and this has become an emerging area of research in dental artificial intelligence. Orthodontic treatment decisions, such as whether to extract teeth and which ones, are based on a range of clinical factors including patient age, gender, dental crowding, skeletal and dental relationships, arch length discrepancies, cephalometric measurements, and facial profiles. These factors can be quantified and used as input features in machine learning models. By training algorithms such as logistic regression, decision trees, support vector machines, or neural networks on historical patient data with known extraction outcomes, it is possible to predict extraction patterns with considerable accuracy. Some studies have also incorporated deep learning techniques using lateral cephalometric radiographs, showing potential for expert-level performance in decision-making. These models not only help standardize treatment planning but also support less experienced clinicians by providing data-driven insights. However, challenges remain, such as the need for high-quality, labeled datasets and the inherently subjective nature of some clinical decisions. Despite these limitations, machine learning offers a promising tool for enhancing efficiency and consistency in orthodontic treatment planning, serving as a valuable aid rather than a replacement for clinical expertise.

The alignment of tooth is a regorous process and needs significant intervention of Dental professionals to perform the task. The clear and blurless images with wider lines of acurate length and width should be considered for both front and rare side of the misalligned teeth of a person for such a complex process. These images aids to identify the regular patterns and spaces exists in the current state of the tooth alignment. There are two possibilities of such misallignment includes widening gap to cause long term problem in teens/adults and a consistent pattern of multiple tooth misallignment pattern in some adults. These distinct patterns can be send to a Machine Learning model such as classification to identify does the pattern label represents a misallignment due to gap between tooth or missing tooth of various reasons. The classification algorithms like SVM, Bayesian inference probabilistic method, multi label classification techniques significantly helps to label the classifiers of the problem. In case of assigning these spaces with an unpredictable gaps due to the patients regorous health problems there could be another factor to be considered to assign weigths as measures of patients health condition parameters in identifying the classifier as part of the Convolutionary Neural Network output generation until the CNN layers result in the expected output.

The article: "Leavitt L, Volovic J, Steinhauer L, Mason T, Eckert G, Dean JA, Dundar MM, Turkkahraman H. Can we predict orthodontic extraction patterns by using machine learning? Orthod Craniofac Res. 2023 Nov;26(4):552-559. doi: 10.1111/ocr.12641. Epub 2023 Mar 9. PMID: 36843547."

Objective: To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population.

Conclusion: All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.