Self-Influence Analysis in Diagnostic Surgeries

Updated 13 May 2026

Self-Influence Analysis is a framework that evaluates how advanced diagnostic surgical systems combine robotics, multimodal sensing, and real-time analytics to optimize performance.
Recent innovations demonstrate sub-millimetric targeting in robotic needle biopsies, 3D reconstruction from palpation, and rapid spectroscopic margin assessments.
Integrating precise robotic control, imaging overlays, and feedback-driven algorithms, these systems reduce sampling errors and enhance intraoperative decision-making.

Diagnostic surgeries encompass a spectrum of interventional procedures aimed at the identification and characterization of pathological tissue via direct sampling, biosensing, imaging, and real-time feedback modalities. The advent of advanced robotics, device miniaturization, and multimodal sensing has led to a range of techniques that transcend the limitations of conventional visual inspection and manual biopsy, offering increased precision, intraoperative intelligence, and integration with computational decision-support systems.

1. Robotic-Assisted Needle Biopsy and Spectroscopy Platforms

Recent diagnostic surgical systems achieve high-precision tissue targeting and sampling through composite robotic architectures integrating gross positioning manipulators with specialized insertion modules. A principal example utilizes a 5-degree-of-freedom (DOF) KUKA youBot arm, providing macroscopic workspace manipulation, coupled with a 3-DOF standalone tool-insertion unit for fine control of insertion depth, tool rotation, and pitch adjustment (Wang et al., 30 Aug 2025).

Forward kinematics of the manipulator are given by $x = f(q_m)$ , with task-space pose $x \in \mathbb{R}^6$ and joint vector $q_m \in \mathbb{R}^5$ . Redundant actuation enables sub-millimetric positioning accuracy, with mean tracking errors $<1$ mm. The insertion subsystem accommodates standard biopsy needles (1.7 mm diameter) and optical fibers (down to 0.2 mm), permitting both tissue extraction and in situ vibrational (Raman/IR) spectroscopy. Tool-axis feed is governed by $x_t = r_2 \phi_2$ (roller-driven translation), and pitch control is mapped via a gear-specific $g(\phi_p)$ .

Control strategies combine resolved-acceleration inverse dynamics ( $\tau_c = M(q_m)\ddot{q}_c + C(q_m, \dot{q}_m)\dot{q}_m + G(q_m)$ ) and task-space admittance for haptic surgeon guidance. For insertion, a force-augmented PD law ( $u = k_p\tilde{x}_t + k_d\dot{\tilde{x}}_t + k_o F_t$ ) balances trajectory tracking and resistance regulation.

Key performance metrics:

Submillimeter platform drift ( $<0.5$ mm under manual perturbation).
Insertion error $<2\%$ of commanded 10 mm depth across synthetic tissue stacks.
Biopsy reliability $x \in \mathbb{R}^6$ 0 hit-rate on phantom targets; rapid multi-depth sampling ( $x \in \mathbb{R}^6$ 1 min for multi-site).

Integrated fiber-optic spectroscopy yields immediate molecular readouts (e.g., lipid vs. collagen) post-insertion, enabling intraoperative triage and reducing the volume of tissue sent for ex vivo histology by $x \in \mathbb{R}^6$ 2. Compared to manual biopsy (typical error $x \in \mathbb{R}^6$ 3– $x \in \mathbb{R}^6$ 4 mm, $x \in \mathbb{R}^6$ 5 hit rate), robotics delivers superior accuracy and sampling confidence (Wang et al., 30 Aug 2025).

2. Palpation-Based Robotic Tumor Localization and 3D Reconstruction

Robotic palpation systems restore haptic discrimination lost in minimally invasive surgery, utilizing force-sensing probes to localize, reconstruct, and assess subdermal tumor boundaries. A representative system consists of a 7-DOF manipulator equipped with a tri-axial load cell and a real-time depth camera to mesh the surgical surface (Uppuluri et al., 2024).

The system models tissue stiffness as a latent field $x \in \mathbb{R}^6$ 6, learned via Gaussian-process Bayesian optimization (BO). Palpation sites are selected by maximizing an upper-confidence-bound acquisition function $x \in \mathbb{R}^6$ 7, which efficiently steers exploration toward likely tumor regions.

After initial detection, continuous contour following is achieved with a spring-damper impedance controller:

$x \in \mathbb{R}^6$ 8

ensuring safe, contact-limited exploration along tumor boundaries.

3D surface reconstruction is performed by projecting trajectory points $x \in \mathbb{R}^6$ 9 onto the precomputed surface mesh, correcting for indentation depth. This approach achieves root mean square reconstruction error $q_m \in \mathbb{R}^5$ 0 mm with $q_m \in \mathbb{R}^5$ 1 palpations (versus $q_m \in \mathbb{R}^5$ 2 for dense 2D mapping with only planar output), and F-score $q_m \in \mathbb{R}^5$ 3 on complex shapes. The method is validated on multi-layer skin-fat-muscle phantoms with embedded rigid tumors (Uppuluri et al., 2024).

Assumptions of isotropic, linearly elastic tissue and rigid inclusions are current limitations; extension to viscoelastic and soft tumors remains an area of ongoing research.

3. Intraoperative Spectroscopic and Dielectric Margin Assessment

Permittivity-based contrast has emerged for label-free delineation of malignant from healthy tissue during colon cancer surgery. Using an open-ended coaxial probe (0.5–26.5 GHz) and matched calibration, surgeons record the differential complex permittivity: $q_m \in \mathbb{R}^5$ 4 For advanced tumor stages (T4a/b), $q_m \in \mathbb{R}^5$ 5 exceeds $q_m \in \mathbb{R}^5$ 6 at $q_m \in \mathbb{R}^5$ 7 GHz, with maximum observed difference up to $q_m \in \mathbb{R}^5$ 8 ( $q_m \in \mathbb{R}^5$ 9) and $<1$ 0 ( $<1$ 1) in ex vivo T4b at $<1$ 2 GHz (Micó-Rosa et al., 27 Jan 2026).

Stage-dependent permittivity thresholds (>3 at $<1$ 3 GHz or >1 at $<1$ 4 GHz) can be used to intraoperatively flag malignant margins and guide targeted biopsy. Real-time processing permits overlay of suspicious zones onto endoscopic video. Limitations include millimeter-scale penetration depth, sensitivity to probe pressure and hydration, and the need for expanded training datasets to refine discriminative models.

4. Real-Time Intraoperative Fluorescence for Tissue Characterization

Dynamic fluorescence imaging, specifically rapid indocyanine green (ICG) perfusion video, complements anatomical assessment by capturing hemodynamic and microvascular signatures of neoplastic tissue. Dual-mode (RGB and NIR) camera systems record the first minutes post-ICG injection, followed by motion-compensated ROI extraction and feature-based classification.

Parametric perfusion fits utilize a second-order ODE: $<1$ 5 yielding feature vectors $<1$ 6. Simple features include time-to-peak, half-rise time, inflow/outflow slopes, and area under the curve. Explainable classifiers (kNN, Naïve Bayes, Decision Tree) attain accuracy up to $<1$ 7 and specificity $<1$ 8 (on a 20-patient rectal lesion cohort). Visualization overlays display perfusion-derived malignancy confidence directly on the surgical display, reducing interobserver variability ( $<1$ 9) (Epperlein et al., 2022).

This generalizable workflow supports assessment of anastomotic viability, sentinel node mapping, and organ transplant monitoring.

5. Image-Guided Radioguided Probe Localization

Radioguided diagnostic resection, notably sentinel lymph node mapping, is enhanced by fusing non-imaging gamma probe feedback with real-time laparoscopic video via deep learning regression. A stereo laparoscope with co-registered gamma detector and known tool pose ( $x_t = r_2 \phi_2$ 0) enables projection of the probe’s sensing field onto the video frame (Huang et al., 2023).

A regression neural network $x_t = r_2 \phi_2$ 1 (with $x_t = r_2 \phi_2$ 2 as ResNet-18-derived visual features) predicts the 2D position of the probe’s field-of-view. On annotated datasets, the regression approach achieves 0% detection failure and mean localization error <2 mm (surgical scale), with IoU of 0.72 for a 15x15 px window—exceeding segmentation baselines. Automated overlays of the probe’s active sensing region and radioactivity counts enhance intraoperative spatial awareness and resection accuracy, while remaining robust to variations in tissue, illumination, and probe orientation (Huang et al., 2023).

6. Photoacoustic and Label-Free Histological Techniques

Intraoperative margin control, particularly in Mohs micrographic surgery, demands near-real-time, high-resolution assessment of excised layers. Photoacoustic Remote Sensing (PARS) microscopy enables non-contact, label-free histological imaging by exploiting endogenous nuclear absorption of UV pulses (266 nm), with detection via a co-focused IR beam (Ecclestone et al., 2020).

Key parameters:

Lateral resolution: $x_t = r_2 \phi_2$ 3 nm (determined by point-spread analysis on nanoparticles).
Whole-margin gross scans: > $x_t = r_2 \phi_2$ 4 cm $x_t = r_2 \phi_2$ 5 in 8–12 min at $x_t = r_2 \phi_2$ 6 µm sampling.
High-resolution regional scans: $x_t = r_2 \phi_2$ 7– $x_t = r_2 \phi_2$ 8 µm per pixel, single-nucleus detail.

Workflow bypasses or augments classical frozen-section processing; gross scans can be performed in the OR prior to standard staining. "One-to-one" comparison with H&E microscopy demonstrates replication of nuclear atypia, hypercellularity, and architectural patterns essential for oncologic decision-making. Optimization directions include higher repetition-rate sources, multiplexed excitation for chromophore-specific imaging, and AI-based pattern recognition. Current limitations involve axial resolution (in thin slices), scanning speed, and tissue section flatness artifacts (Ecclestone et al., 2020).

7. Robotics, Capsule, and Magnetically Actuated Diagnostic Modalities

Diagnostic surgeries further leverage advanced robotic platforms including continuum and concentric-tube robots for confined lumen navigation, capsule endoscopy for GI surveillance, and magnetic microrobots for otherwise inaccessible locations (Simaan et al., 2018). These platforms are characterized by the following:

High-DOF architectures (multi-backbone, wire-actuated wrists, concentric tube).
Integration of multi-modal sensors: CCD/CMOS video, IVUS, force and fiber-optic.
Mechanisms for forward and inverse kinematics (constant-curvature models), dynamic modeling (fluid interaction), and control (joint-space PID, impedance, haptic).
Capsule locomotion via propeller, shape-memory, or magnetic actuation.
Limitations in workspace, localization accuracy, haptic feedback, and device miniaturization.

Magnetic actuation is governed by dipole-field equations, with localization via sensor arrays and Kalman filtering. Safety constraints (<2 N force at the tissue interface) and performance metrics (pose error, maneuverability) are established.

Persisting challenges include robust in vivo force sensing, real-time situational co-registration, capsule autonomy, and high-precision, minimally disruptive access to complex anatomies (Simaan et al., 2018).

Diagnostic surgeries are now defined by a convergence of robotic manipulation, multimodal sensing, real-time data analytics, and integrated feedback, supporting more accurate, efficient, and evidence-driven interventions. The integration of these technologies directly addresses historical limitations in targeting accuracy, margin assessment, and intraoperative decision-making, as substantiated across multiple research platforms and clinical studies (Wang et al., 30 Aug 2025, Uppuluri et al., 2024, Huang et al., 2023, Epperlein et al., 2022, Ecclestone et al., 2020, Micó-Rosa et al., 27 Jan 2026, Simaan et al., 2018).