arxiv: 2605.07082 · v1 · submitted 2026-05-08 · 💻 cs.CV

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ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction

Xinquan Yang , Congmin Wang , Xuguang Li , Yulei Li , Linlin Shen , Yongqiang Deng He Meng

Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:27 UTC · model grok-4.3

classification 💻 cs.CV

keywords dental implantposition predictionMambasequential modelingCNN-Mamba hybridslope regressionsurgical guide design

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The pith

ImplantMamba combines CNNs with Mamba selective scans and a slope-coupled branch to predict dental implant positions from surrounding tooth textures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a new network called ImplantMamba to determine precise implant locations and angulations in medical images where the implant site itself has little distinctive texture. It builds a hybrid encoder that uses CNNs for local anatomical details and Mamba layers for long-range dependencies across the full scan volume, letting the model draw on patterns from adjacent teeth. A dedicated Slope-Coupled Prediction branch links the position output directly to the slope output so the two predictions remain consistent with each other and with normal dental anatomy. Experiments on a large dental implant dataset show the model outperforms prior methods.

Core claim

The core of ImplantMamba is a hybrid encoder that combines Convolutional Neural Networks (CNNs) with Mamba layers. This design enables the network to hierarchically extract local anatomical features through CNNs while simultaneously modeling global contextual dependencies across the entire scan volume via Mamba's selective scan operations, leading to a more comprehensive understanding of the implant site. Furthermore, we introduce a Slope-Coupled Prediction Branch (SCP). This branch is designed to connect the prediction of implant position with the slope, ensuring internal consistency and anatomical plausibility by thereby enforcing a coherent relationship between the predicted implant locat

What carries the argument

Hybrid CNN-Mamba encoder with selective-scan operations plus the Slope-Coupled Prediction (SCP) branch that jointly regresses implant position and angulation.

If this is right

The model produces implant position and slope predictions that maintain internal consistency with dental anatomy.
Long-range context from adjacent teeth improves accuracy in regions with low local texture.
Superior performance on large-scale dental implant datasets compared with existing methods.
The architecture supports hierarchical local feature extraction combined with global scan-volume modeling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar hybrid encoders could be tested on other medical imaging tasks that require inferring object placement from distant contextual cues.
The SCP coupling idea might generalize to other paired regression problems where one output constrains another.
If the Mamba component scales well to full 3D volumes, it could reduce the need for heavy transformer-based alternatives in volumetric medical prediction.

Load-bearing premise

That explicitly coupling position regression with slope regression via the SCP branch will enforce anatomical plausibility and that Mamba selective scans will successfully integrate texture information from adjacent teeth across the scan volume.

What would settle it

Run the trained model on a test set where texture from neighboring teeth is blurred or masked and measure whether position and slope errors increase sharply relative to the unaltered test set.

Figures

Figures reproduced from arXiv: 2605.07082 by Congmin Wang, Linlin Shen, Xinquan Yang, Xuguang Li, Yongqiang Deng He Meng, Yulei Li.

**Figure 1.** Figure 1: Overview of the proposed ImplantMamba. 2 ImplantMamba An overview of the proposed ImplantMamba is given in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: Visualization of the predicted implant on the ImplantFairy dataset. The white and green masks represent the predicted implant and the actual implant, respectively. 3.2 Performance Analysis Ablation Studies. To evaluate the impact of integrating the proposed ConvMamba block into the hybrid encoder, we conducted an ablation study by progressively integrating them across the encoder’s layers, as detailed in… view at source ↗

read the original abstract

In the design of surgical guides for implant placement, determining the precise implant position is a critical step. However, the implant region itself is often characterized by a lack of distinctive texture in medical images. Consequently, artificial intelligence (AI) models must infer the correct implant position and angulation (slope) primarily by analyzing the texture of the surrounding teeth, which poses a significant challenge. To address this, we propose ImplantMamba, a network architecture designed for long-range sequential modeling to integrate texture information from adjacent teeth. Our approach explicitly couples the regression of the implant position with its slope. The core of ImplantMamba is a hybrid encoder that combines Convolutional Neural Networks (CNNs) with Mamba layers. This design enables the network to hierarchically extract local anatomical features through CNNs while simultaneously modeling global contextual dependencies across the entire scan volume via Mamba's selective scan operations, leading to a more comprehensive understanding of the implant site. Furthermore, we introduce a Slope-Coupled Prediction Branch (SCP). This branch is designed to connect the prediction of implant position with the slope, ensuring internal consistency and anatomical plausibility by thereby enforcing a coherent relationship between the predicted implant location and its angulation. Extensive experiments on a large-scale dental implant dataset demonstrate that the proposed ImplantMamba achieves superior performance compared to existing methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ImplantMamba pairs a CNN-Mamba hybrid encoder with a slope-coupled prediction branch to handle texture-less implant sites by borrowing from adjacent teeth, but the experiments supply no ablations or auxiliary checks to show those pieces actually produce the gains.

read the letter

The paper's core idea is straightforward: dental implant sites often lack distinctive texture in scans, so the model must infer position and angulation from neighboring teeth. ImplantMamba uses CNN layers for local features and Mamba selective scans for longer-range context across the volume, then adds a Slope-Coupled Prediction Branch that ties the position regression directly to the slope output to keep results anatomically consistent. That framing and the hybrid design are the main new elements relative to prior dental imaging work. The motivation is clear and the architecture description is concrete enough to follow without extra background. The coupling mechanism is a simple, direct way to enforce the position-slope relationship that clinicians care about. The soft spots sit in the validation. The abstract states superior performance on a large dataset, yet the provided details give no numbers, no baseline comparisons, no error bars, and no ablations that remove the SCP branch or the Mamba layers. There are also no auxiliary metrics, such as measured correlation between predicted position and slope or checks on how well neighboring tooth textures are actually used. Without those, the performance delta could trace to dataset size, training choices, or the CNN backbone alone rather than the claimed mechanisms. The stress-test concern about unvalidated assumptions holds here. This is a narrow-scope paper aimed at dental imaging researchers or groups adapting Mamba to medical volumes with strong anatomical constraints. A reader outside that niche would get little beyond the application details. I would send it to peer review because the clinical task is well-defined and the design choices are specific enough for referees to assess the missing controls and tighten the claims.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes ImplantMamba, a hybrid CNN-Mamba encoder with a Slope-Coupled Prediction (SCP) branch for regressing dental implant position and angulation from CBCT volumes. It claims that Mamba's selective state-space scans enable long-range integration of texture cues from adjacent teeth (where the implant site itself lacks distinctive features) and that explicit position-slope coupling in the SCP branch enforces anatomical consistency, yielding superior performance over prior methods on a large-scale dental dataset.

Significance. If the performance gains are shown to arise specifically from the Mamba long-range modeling and the SCP coupling rather than from the CNN backbone or training protocol, the work would offer a targeted architectural solution to a recurring challenge in dental implant planning. The inductive bias of coupling position and slope is a plausible way to improve plausibility, and successful demonstration could influence other medical imaging tasks that require contextual inference across texture-poor regions.

major comments (3)

[Experiments section] Experiments section: The central claim of 'superior performance' is asserted without any reported quantitative metrics (position error, slope error, success rates), error bars, dataset size, train/test split, or baseline implementations. This absence makes it impossible to assess whether the hybrid encoder or SCP branch actually drives improvement.
[Section 3.2] Section 3.2 (SCP Branch): The assertion that coupling position and slope 'ensures internal consistency and anatomical plausibility' is not accompanied by any supporting analysis, such as predicted position-slope correlation on ground truth versus model outputs, or an ablation replacing the SCP branch with independent regression heads. Without these checks the coupling remains an unverified design choice rather than a demonstrated mechanism.
[Section 3.1] Section 3.1 (Hybrid Encoder): The motivation that Mamba selective scans successfully propagate texture information from neighboring teeth is stated qualitatively, yet no ablation (Mamba layers removed), feature-map visualization, or auxiliary metric (e.g., intersection-with-bone rate) is provided to confirm that long-range dependencies are operative and beneficial for the implant-site prediction.

minor comments (2)

[Abstract] Abstract: The phrase 'large-scale dental implant dataset' should be replaced or supplemented with concrete numbers (number of volumes, patients, annotation protocol) to allow readers to gauge scale and reproducibility.
[Method] Method: The SCP branch is described at a high level; a concise equation or diagram showing exactly how the position and slope heads share features and enforce consistency would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive review of our manuscript. We agree that the current version lacks sufficient quantitative evidence and ablations to fully support our claims. We will revise the manuscript to include all requested metrics, analyses, and ablations as detailed in our point-by-point responses below.

read point-by-point responses

Referee: [Experiments section] Experiments section: The central claim of 'superior performance' is asserted without any reported quantitative metrics (position error, slope error, success rates), error bars, dataset size, train/test split, or baseline implementations. This absence makes it impossible to assess whether the hybrid encoder or SCP branch actually drives improvement.

Authors: We acknowledge that the manuscript as currently presented does not include the quantitative results, which is an important omission. In the revised version, we will report all relevant metrics including position error (e.g., Euclidean distance in mm), slope error (angular deviation in degrees), success rates based on clinical thresholds, with standard deviations or error bars across multiple runs or folds. We will specify the dataset size, train/validation/test splits, and provide details on baseline implementations for fair comparison. This will allow readers to evaluate the contributions of the hybrid encoder and SCP branch. revision: yes
Referee: [Section 3.2] Section 3.2 (SCP Branch): The assertion that coupling position and slope 'ensures internal consistency and anatomical plausibility' is not accompanied by any supporting analysis, such as predicted position-slope correlation on ground truth versus model outputs, or an ablation replacing the SCP branch with independent regression heads. Without these checks the coupling remains an unverified design choice rather than a demonstrated mechanism.

Authors: We agree that the benefit of the Slope-Coupled Prediction branch requires empirical validation beyond the qualitative motivation. In the revision, we will add a correlation analysis comparing the position-slope relationship in ground truth data to that in model predictions. Additionally, we will include an ablation study where the SCP branch is replaced with separate independent heads for position and slope regression, and compare performance to demonstrate the advantage of the coupling in enforcing consistency. revision: yes
Referee: [Section 3.1] Section 3.1 (Hybrid Encoder): The motivation that Mamba selective scans successfully propagate texture information from neighboring teeth is stated qualitatively, yet no ablation (Mamba layers removed), feature-map visualization, or auxiliary metric (e.g., intersection-with-bone rate) is provided to confirm that long-range dependencies are operative and beneficial for the implant-site prediction.

Authors: To substantiate the role of the Mamba layers in long-range modeling, we will perform an ablation experiment by removing the Mamba components and relying solely on the CNN encoder, reporting the resulting performance drop. We will also include visualizations of feature maps or state activations to illustrate how information from adjacent teeth influences the implant site prediction. Furthermore, we will introduce an auxiliary metric such as the intersection-with-bone rate to quantify the anatomical plausibility and show the benefit of global context integration. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical architecture with no derivations or self-referential predictions

full rationale

The paper describes a hybrid CNN-Mamba network plus SCP branch for implant position/slope regression and reports superior empirical results on a dental dataset. No equations, first-principles derivations, or parameter-fitting steps are presented that could reduce any claimed output to an input by construction. Architectural motivations (long-range texture integration via Mamba scans, explicit position-slope coupling) remain descriptive and are not shown to be equivalent to the performance metric itself. Self-citations, if present, are not load-bearing for any core claim. The result is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the unstated premise that Mamba layers can capture clinically relevant long-range dental textures and that position-slope coupling improves plausibility; no explicit axioms, free parameters, or invented entities are declared in the abstract.

pith-pipeline@v0.9.0 · 5552 in / 1082 out tokens · 52313 ms · 2026-05-11T01:27:28.998339+00:00 · methodology

discussion (0)

Reference graph

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