Refractive Surgery : Phakic intraocular lens (APL) Sizing Role of Anatomical Parameters, Advanced Imaging Technologies, and Artificial Intelligence–Based Prediction

2. Concept of Vault in Phakic intraocular lens (APL)

Vault is defined as:

The distance between the posterior surface of Phakic (APL) and the anterior crystalline lens.

Typical clinical interpretation:

vault range	interpretation
<200 µm	low vault
300–700 µm	optimal
700–1000 µm	acceptable
>1000 µm	high vault

Vault reflects compatibility between lens size and internal ocular anatomy

3. Traditional Method of Phakic intraocular lens (APL) Sizing

3.1 White-to-White (WTW)

WTW measures the horizontal corneal diameter between the nasal and temporal limbal boundaries and is commonly used as a primary parameter in ICL sizing due to its simplicity and accessibility. The main advantages of WTW include ease of measurement, non-contact acquisition, and widespread availability across most optical biometers and corneal imaging systems. However, an important limitation of WTW is that it represents an external anatomical dimension and does not directly correspond to the internal ciliary sulcus diameter, where the ICL haptics are positioned. Consequently, reliance on WTW alone may not fully capture individual variations in internal ocular anatomy that influence postoperative vault.

3.2 Anterior Chamber Depth (ACD)

Anterior Chamber Depth (ACD) represents the distance between the posterior corneal surface (endothelium) and the anterior surface of the crystalline lens. ACD reflects the available internal space in the anterior segment for placement of the Phakic (APL) lens.

ACD is influenced by:

corneal curvature
crystalline lens position
age-related lens thickening
axial length

A deeper anterior chamber typically provides greater internal volume for lens positioning and may influence vault behaviour.

3.3 Limitations of the Traditional Method

The traditional combination of white-to-white (WTW) and anterior chamber depth (ACD) may not adequately represent the complex variability of internal anterior segment anatomy. Since these parameters primarily reflect external or indirect anatomical dimensions, they may fail to capture individual differences in sulcus size, lens rise, and ciliary body configuration. As a result, clinical outcomes may demonstrate variability in postoperative vault, including cases of unexpectedly high vault or unexpectedly low vault. Additionally, noticeable inter-eye variability may occur even when preoperative biometric measurements appear similar, highlighting the limitations of relying solely on WTW and ACD for accurate ICL sizing.

4. Modern Anatomical Parameters for Phakic (APL) Sizing

4.1 Angle-to-Angle (ATA)

4.1.1. Definition and Anatomical Concept

Angle-to-Angle (ATA) represents the internal horizontal distance between the scleral spurs across the anterior chamber. The scleral spur is an anatomical landmark located at the junction between the cornea and sclera, forming part of the anterior chamber angle structure.

ATA measurement provides a more anatomically relevant estimation of internal ocular width compared with WTW because it evaluates internal structures closer to the anatomical location where Phakic (APL) lenses interact with surrounding tissues.

While WTW measures the external corneal diameter, ATA reflects the internal structural width of the anterior chamber, providing additional information regarding spatial geometry of the anterior segment.

4.1.2. Measurement Method

ATA is typically measured using:

Anterior Segment OCT
Swept-source OCT
Combined Placido-OCT imaging systems

These devices identify scleral spur landmarks across multiple meridians and calculate the internal anterior chamber diameter.

Clinical Importance in Phakic (APL) Sizing

ATA provides an improved anatomical representation of the internal ocular dimension compared with external corneal measurements. Differences between WTW and ATA values may indicate variation in scleral thickness, angle configuration, or anterior segment morphology.

ATA is particularly useful in cases where:

WTW values appear borderline
External measurements show inter-eye symmetry, but the vault differs
Anterior chamber geometry appears atypical

ATA measurement helps refine the estimation of internal ocular width and may contribute to improved lens size selection.

4.1.3. Relationship between ATA and Vault

: A larger ATA may indicate a larger internal anatomical width and may be associated with a lower vault if lens size is relatively small.

A smaller ATA may indicate narrower internal anatomy and may be associated with a higher vault when lens size is relatively large.

ATA improves understanding of spatial anatomy, influencing vault behaviour.

4.2 Sulcus-to-Sulcus (STS)

4.2.1. Definition and Anatomical Concept

Sulcus-to-Sulcus (STS) represents the internal diameter between opposing ciliary sulcus structures, corresponding directly to the anatomical location where Phakic (APL) haptics rest.

The ciliary sulcus is located posterior to the iris root and anterior to the ciliary body. Because the Phakic (APL) lens is supported within this anatomical space, STS measurement provides a direct representation of the anatomical dimension most relevant to lens sizing.

4.2.3. Measurement Method

STS is typically measured using:

Ultrasound Biomicroscopy (UBM)
high-resolution anterior segment imaging techniques capable of visualizing posterior iris structures

UBM imaging allows visualisation of the ciliary body and sulcus morphology, enabling measurement of the internal diameter relevant to lens positioning.

4.2.4. Clinical Importance in Phakic (APL) Sizing

STS is considered one of the most anatomically relevant parameters for predicting postoperative vault because it directly represents the internal support structure of the lens.

Variation in STS diameter explains why eyes with similar WTW values may demonstrate different vault outcomes.

STS measurement is particularly valuable in cases demonstrating:

unexpected vault variation
large refractive error
anatomical asymmetry
previous lens exchange history

4.2.5. Relationship between STS and Vault

: A larger STS diameter may result in a lower vault if the lens size is relatively small.

A smaller STS diameter may result in a higher vault if the lens size is relatively large.

Direct measurement of sulcus anatomy improves the predictability of effective lens fit.

4.3 Lens Rise

4.3.1 Definition and Anatomical Concept

Lens rise represents the perpendicular distance between the anterior surface of the crystalline lens and a reference line connecting the scleral spurs (angle plane).

Lens rise describes the relative anterior positioning of the crystalline lens within the eye and provides information about how much the crystalline lens protrudes into the anterior chamber space.

Lens rise may be positive or negative, depending on the relative position of the crystalline lens apex with respect to the angle reference plane.

4.3.2. Measurement Method

Lens rise is measured using:

Anterior segment OCT
Scheimpflug imaging with angle reference analysis
combined anterior segment imaging systems

These devices identify the scleral spur plane and calculate the relative position of the crystalline lens apex.

4.3.3. Clinical Importance in Phakic (APL) Sizing

Lens rise influences the effective space available between the crystalline lens and phakic lens. Eyes with higher lens rise demonstrate reduced internal space, which may influence postoperative vault behaviour even when WTW and ATA values appear similar.

Lens rise is particularly important in cases where:

ACD appears adequate, but vault prediction remains uncertain
Age-related lens thickening occurs
An unexpectedly low vault is observed despite an appropriate lens size

Relationship between Lens Rise and Vault

higher lens rise → reduced effective internal space → tendency toward lower vault

lower lens rise → increased internal space → tendency toward higher vault

Lens rise helps explain variability in vault outcomes between eyes with similar external dimensions.

5. Advanced Technologies Used to Measure Phakic (APL) Size

5.1 Optical Coherence Tomography (Anterior Segment OCT)

Anterior Segment Optical Coherence Tomography (AS-OCT) is one of the most important modern diagnostic technologies used in Phakic (APL) sizing. OCT uses low-coherence interferometry to generate high-resolution cross-sectional images of the anterior segment, allowing detailed visualisation of anatomical structures relevant to lens sizing.

Key anatomical parameters measured using AS-OCT

Angle-to-Angle (ATA)
The distance between scleral spurs across the anterior chamber.

ATA provides better internal anatomical estimation compared to WTW.

Anterior Chamber Depth (ACD)
distance from the endothelium to the crystalline lens.

determines available internal space for lens placement.

Lens Rise
The distance between the crystalline lens apex and angle reference plane.

important determinant of vault behaviour.

Angle anatomy
evaluation of angle width and configuration.

helps predict the risk of angle crowding in high vault cases.

Advantages of Phakic (APL) sizing

non-contact measurement
high repeatability
high spatial resolution
visualisation of internal anatomy
good patient comfort

These devices provide detailed anatomical mapping useful for personalised lens sizing.

5.2 Ultrasound Biomicroscopy (UBM)

Ultrasound biomicroscopy utilises high-frequency ultrasound waves (35–50 MHz) to image structures posterior to the iris that are not easily visualised by optical methods.

UBM remains one of the most anatomically direct methods for evaluating internal ocular structures relevant to Phakic (APL) sizing.

Anatomical parameters obtained using UBM

Sulcus-to-Sulcus (STS)
direct measurement of ciliary sulcus diameter.

represents the anatomical resting location of Phakic (APL) haptics.

Ciliary body configuration
The shape and orientation of ciliary processes influence lens positioning.

Iris insertion profile
affects lens centration and vault stability.

Posterior chamber morphology
helps identify anatomical variations influencing the vault.

Clinical significance

STS measurement provides a direct anatomical reference for lens sizing.

particularly useful when:

WTW borderline
Unexpected vault previously observed
high myopia cases
Anatomical asymmetry suspected

Advantages

direct visualisation of sulcus anatomy
improved anatomical accuracy
useful in complex cases

Limitations

contact or immersion technique required
operator dependent
limited availability in routine clinical settings

5.3 Scheimpflug Imaging (Corneal Tomography)

Scheimpflug imaging systems use a rotating camera and slit illumination to create a three-dimensional reconstruction of the anterior segment.

Scheimpflug tomography is primarily used for corneal assessment, but also provides useful parameters for Phakic (APL) sizing.

Parameters relevant to Phakic sizing

WTW measurement
ACD measurement
anterior chamber volume
angle estimation
corneal health assessment

5.4 Optical Biometry

Optical biometers use infrared interferometry to measure ocular dimensions.

Although originally developed for cataract surgery planning, optical biometers provide useful parameters for Phakic (APL) sizing.

Parameters obtained

WTW
ACD
axial length
keratometry

Clinical relevance

: optical biometry provides standardised measurement of WTW and ACD with high repeatability.

often used as a baseline measurement before advanced imaging.

6. Role of Artificial Intelligence in Phakic (APL) Sizing

Artificial Intelligence (AI) is increasingly transforming the methodology of Phakic (APL) sizing by enabling integration of multiple anatomical and biometric parameters into predictive models capable of estimating postoperative vault with improved precision. Traditional sizing approaches rely primarily on limited variables such as WTW and ACD, whereas AI-based systems analyse complex, nonlinear relationships between multiple anatomical factors that influence lens positioning within the posterior chamber.

Modern AI algorithms utilise machine learning (ML), deep learning, and multivariable regression models trained on large clinical datasets containing preoperative biometric parameters and postoperative vault outcomes. These models identify patterns between internal ocular anatomy and vault behaviour that may not be evident using conventional nomograms. AI-based prediction has demonstrated improved performance in predicting postoperative vault and optimising lens size selection compared with traditional estimation methods.

6.1.1. Currently Available AI-based Technologies and Platforms

Several AI-assisted platforms and research-based tools are currently available or emerging in clinical practice for Phakic (APL) sizing:

6.1.2. ICL Guru (RevAI platform)

One of the most advanced AI-driven vault prediction systems uses deep learning models trained on ultrasound biomicroscopy (UBM) image datasets to predict postoperative vault behaviour. The system analyses anatomical parameters, including internal anterior segment geometry, anterior chamber depth, and sulcus configuration, to generate a predicted vault range for different lens sizes.

A key feature of this approach is the ability to analyse multiple image frames from UBM scans, automatically identify optimal anatomical landmarks, and simulate expected vault outcomes. AI-based frame selection improves measurement consistency and reduces operator dependency.

6.2. Machine Learning Vault Prediction Models

Recent studies have demonstrated the use of machine learning techniques such as:

• Random forest models
• Gradient boosting (XGBoost)
• multivariable regression algorithms
• neural networks

These models utilise combinations of anatomical parameters, including WTW, ATA, STS, lens rise, keratometry, and age, to predict vault outcomes more accurately than traditional formulas.

Machine learning approaches have shown improved accuracy in predicting vault variability, especially in anatomically borderline eyes.

6.3 Multimodal AI-based Biometric Integration

Modern AI tools increasingly integrate data from multiple imaging modalities:

OCT images
UBM images
Scheimpflug tomography
biometric measurements

This multimodal integration improves prediction reliability by combining structural and biometric information into a unified analytical model.

AI models can simultaneously analyse:

internal anterior segment width
lens curvature and position
angle configuration
anterior chamber volume
biomechanical relationships between structures

Such multivariable integration allows more individualised prediction of vault behaviour.

7. Workflow of AI-assisted Phakic (APL) sizing

Step 1
input biometric data including WTW, ACD, ATA, STS, keratometry and refraction.

Step 2
An AI algorithm compares input parameters with a database of previously implanted cases.

Step 3
The predictive model estimates the vault range for each available lens diameter.

Step 4
The clinician selects the lens size corresponding to the optimal predicted vault range.

Some platforms also provide confidence intervals or safety bands indicating the probability of achieving the target vault range.

7.1. Advantages of AI-based Phakic (APL) sizing

AI-based systems provide several clinical advantages compared with traditional sizing approaches:

improved vault predictability
reduction in extreme vault outcomes
improved inter-eye symmetry
improved reproducibility of sizing decisions
reduction in dependence on a single surrogate measurement
Improved personalisation of lens selection

AI models can identify subtle anatomical relationships that influence vault behaviour but may not be evident from isolated measurements.

AI-based systems are particularly beneficial in cases with:

borderline WTW values
borderline ACD
large refractive error
unusual anterior segment anatomy
previous unexpected vault outcomes

7.2. Current Limitations of AI Technology

Despite promising results, AI-based sizing systems face certain limitations:

dependence on the quality of imaging data
variability in device measurement standards
limited availability of large standardised datasets
regulatory and validation requirements

Further multicentre validation studies are required to ensure universal applicability across diverse populations.

7.2.3. Clinical significance of AI integration

AI-assisted Phakic (APL) sizing represents an evolution from empirical estimation toward data-driven personalised refractive surgery planning. Integration of AI with advanced imaging technologies enables a more comprehensive understanding of internal ocular anatomy and supports improved consistency in vault outcomes.

As datasets continue to expand and predictive algorithms improve, AI-based systems are expected to play an increasingly important role in enhancing safety, predictability, and clinical confidence in Phakic (APL) sizing workflows.

8. 3D Anterior Segment Modelling for Phakic (APL) Sizing

What it is, availability, and role in ICL sizing

What is 3D Anterior Segment Modelling?

8.1. Background and Rationale

Accurate sizing of Phakic (APL) lenses requires detailed understanding of the spatial anatomical relationships within the anterior segment of the eye. Traditional measurement techniques rely primarily on linear parameters such as White-to-White (WTW) and Anterior Chamber Depth (ACD), which provide indirect estimation of internal anatomical structures. However, Phakic (APL) vault behaviour is influenced by three-dimensional anatomical characteristics including sulcus morphology, iris configuration, crystalline lens position, and anterior chamber geometry.

Three-dimensional (3D) anterior segment modelling enables volumetric reconstruction of the anterior segment, allowing comprehensive visualization of anatomical relationships between the cornea, iris, crystalline lens, anterior chamber angle, and ciliary sulcus. Unlike single-plane cross-sectional imaging, 3D modelling provides spatial representation of the anatomical environment in which the phakic lens is positioned.

Understanding the three-dimensional configuration of the anterior segment is important because vault behaviour depends on spatial compatibility between lens geometry and ocular anatomy rather than a single linear measurement.

8.1.1 Imaging Technology Used for 3D Reconstruction

3D anterior segment modelling is typically performed using high-resolution imaging technologies capable of acquiring multiple cross-sectional scans across different meridians of the anterior segment.

Common imaging modalities include:

Swept-source anterior segment Optical Coherence Tomography (SS-OCT)
Spectral-domain anterior segment OCT
Scheimpflug tomographic imaging
Combined Placido-OCT imaging systems

These technologies acquire sequential two-dimensional cross-sectional images of the anterior segment at multiple angles. Software algorithms then reconstruct volumetric datasets that represent the three-dimensional geometry of the anterior segment.

Examples of currently available clinical platforms capable of multi-meridian imaging include:

MS-39 anterior segment OCT
Casia swept-source OCT
Anterion OCT imaging platform
Pentacam AXL tomography system

These devices provide high-resolution imaging of anatomical structures relevant to Phakic (APL) sizing.

8.1.2. Method of 3D Anatomical Reconstruction

The process of generating a 3D anterior segment model involves multiple sequential steps:

Step 1 – Image acquisition
Multiple radial cross-sectional scans of the anterior segment are obtained across different meridians using OCT or Scheimpflug imaging.

Step 2 – Landmark identification
Key anatomical landmarks including scleral spurs, anterior chamber angle structures, corneal boundaries, and crystalline lens surfaces are automatically detected using image processing algorithms.

Step 3 – Digital reconstruction
Software reconstructs a volumetric model by integrating multiple cross-sectional images into a continuous anatomical structure.

Step 4 – Geometric analysis
Quantitative measurements of anterior segment geometry are obtained, including spatial relationships between structures influencing Phakic (APL) positioning.

Step 5 – Predictive modelling
Advanced software may simulate lens positioning and estimate postoperative vault distribution based on anatomical configuration.

This process enables evaluation of anatomical relationships in multiple meridians rather than relying on a single cross-sectional measurement.

8.2. Anatomical Parameters Evaluated Using 3D Modelling

Three-dimensional modelling enables evaluation of anatomical characteristics that influence Phakic (APL) sizing but may not be fully captured using conventional linear measurements.

Parameters that can be assessed include:

anterior chamber volume and shape
three-dimensional angle configuration
crystalline lens curvature and tilt
iris contour and spatial configuration
sulcus geometry and symmetry
relationship between anterior chamber width and lens position
regional anatomical variability between meridians

3D analysis also allows assessment of whether anatomical dimensions are symmetric or asymmetric across different orientations.

Such information is particularly useful when evaluating eyes demonstrating variability in vault outcomes despite similar conventional biometric parameters.

8.3. Relationship Between 3D Anatomy and Vault Behaviour

Vault represents the spatial distance between the posterior surface of the Phakic (APL) lens and the anterior surface of the crystalline lens. Because both structures exhibit curvature and may demonstrate positional variation, vault is influenced by three-dimensional anatomical compatibility.

Factors influencing vault behaviour include:

shape of the ciliary sulcus
anterior positioning of crystalline lens
iris configuration
anterior chamber geometry
regional anatomical asymmetry
lens tilt or decentration

Eyes with similar WTW values may demonstrate different vault outcomes due to differences in internal anatomical configuration that are better visualized using three-dimensional modelling.

3D modelling helps explain variability in vault distribution across different regions of the lens, including central and peripheral vault differences.

8.4. Role of Artificial Intelligence in 3D Anatomical Analysis

Artificial intelligence algorithms can be integrated with 3D anterior segment modelling to improve prediction of Phakic (APL) sizing outcomes. Machine learning models analyze large datasets of anatomical measurements and postoperative vault outcomes to identify relationships between anatomical geometry and lens behaviour.

AI-based analysis enables:

pattern recognition in anatomical variability
prediction of vault distribution
identification of risk factors for high vault or low vault
improved estimation of lens size compatibility
improved consistency in inter-eye prediction

Advanced analytical tools may simulate multiple lens-size options within the reconstructed anatomical model and estimate the expected vault range.

Such predictive capability supports personalised selection of Phakic (APL) size based on patient-specific anatomical characteristics.

8.5. Comparison with Conventional Linear Measurement Approach

Traditional sizing methods rely primarily on external anatomical measurements that approximate internal ocular dimensions. These methods provide limited information regarding three-dimensional anatomical variability.

3D modelling enables improved anatomical representation of the spatial environment influencing Phakic (APL) positioning.

Comparison of approaches:

Parameter	Conventional method	3D modelling approach
anatomical representation	linear measurement	volumetric assessment
sulcus estimation	indirect	anatomical visualization
vault prediction	moderate predictability	improved predictability
detection of asymmetry	limited	multi-meridian evaluation
assessment of tilt	not possible	detectable
individual customization	limited	patient-specific

Three-dimensional modelling provides additional anatomical insight that may improve prediction accuracy in anatomically complex cases.

8.6. Clinical Relevance in Phakic (APL) Sizing

3D anterior segment modelling is particularly valuable in clinical scenarios where conventional measurements provide limited predictive accuracy.

Examples include:

borderline anatomical measurements
significant refractive error
inter-eye variability in vault outcome
atypical anterior chamber geometry
previously observed unexpected vault results

3D anatomical assessment provides additional structural information that may assist in refining lens size selection.

8.9. Current Clinical Availability and Practical Considerations

Although full real-time simulation of Phakic (APL) lens positioning is still evolving, currently available OCT and Scheimpflug imaging platforms provide partial three-dimensional reconstruction of anterior segment anatomy.

These systems are increasingly used in advanced refractive surgery centres to supplement conventional biometric measurements.

Practical considerations include:

availability of imaging technology
requirement for image quality standardisation
variability in measurement algorithms between devices
need for further validation studies

Despite these limitations, the integration of three-dimensional anatomical analysis into clinical workflow is expanding.

9. Summary of Methodological Importance

Three-dimensional anterior segment modelling represents an important advancement in anatomical evaluation for Phakic (APL) sizing.

By enabling volumetric assessment of anterior segment geometry, 3D modelling improves understanding of spatial relationships influencing vault behaviour.

Integration of volumetric imaging with artificial intelligence-based predictive tools may contribute to improved predictability of lens sizing outcomes and supports development of individualized anatomical sizing strategies.

Future advancements in computational imaging may further enhance the precision of personalised Phakic (APL) sizing approaches.

Acknowledgement: All figures are schematic educational representations created or adapted from publicly available scientific concepts for explanatory purposes only. No copyright claim is made over original clinical device images.

10. Conclusion

Accurate selection of Phakic (APL) size in mm remains one of the most critical determinants of achieving optimal postoperative vault, long-term safety, and stable refractive outcomes in posterior chamber phakic lens implantation. Traditional sizing methods based primarily on White-to-White (WTW) and Anterior Chamber Depth (ACD) provide a practical and widely accessible starting point; however, these parameters represent indirect anatomical estimations and may not fully reflect the true internal geometry of the eye where the phakic lens is positioned.

Modern anatomical parameters such as Angle-to-Angle (ATA), Sulcus-to-Sulcus (STS), and Lens Rise provide improved understanding of the internal spatial relationships between the cornea, iris, crystalline lens, and ciliary sulcus. These parameters offer enhanced anatomical relevance because they more closely correspond to the actual location of haptic support and the effective internal space available for vault formation. Variations in sulcus morphology, lens position, and anterior chamber configuration explain why eyes with similar WTW measurements may demonstrate different postoperative vault outcomes.

Advances in imaging technologies, including Anterior Segment OCT, Ultrasound Biomicroscopy (UBM), Scheimpflug tomography, and optical biometry, have significantly improved the ability to visualize and quantify anterior segment anatomy. These modalities enable more reliable identification of anatomical variations that influence lens sizing decisions. The integration of Artificial Intelligence (AI) and multivariable prediction models further enhances the ability to analyse complex relationships between anatomical parameters and postoperative vault behaviour, supporting a transition from empirical estimation toward personalized sizing strategies.

Emerging technologies such as 3D anterior segment modelling provide additional insight into spatial anatomical relationships by enabling volumetric reconstruction of the anterior chamber environment. This approach allows improved visualization of lens position, tilt, vault distribution, and anatomical symmetry between eyes. Such advanced modelling techniques contribute to improved understanding of inter-eye variability and may help refine future nomograms for Phakic (APL) sizing.

Based on current evidence, optimal Phakic (APL) size selection should not rely on a single measurement parameter but should incorporate a multivariable anatomical approach, combining traditional measurements with modern imaging-derived parameters. The combined interpretation of WTW, ACD, ATA, STS, lens rise, and anterior chamber configuration provides a more comprehensive understanding of internal ocular anatomy and improves predictability of vault outcomes.

Continued advancements in imaging resolution, computational modelling, and AI-based prediction systems are expected to further improve the precision and reproducibility of Phakic (APL) sizing. Adoption of integrated anatomical assessment protocols may contribute to enhanced surgical safety, improved refractive predictability, and greater consistency in clinical outcomes.

Overall, the evolution from traditional estimation methods toward advanced anatomical and data-driven approaches represents an important step toward personalized refractive surgery planning and improved long-term performance of Phakic (APL) lenses.

Author:
Sudhanshu Sharma, Marketing Manager, Adaptive Ocular Sciences Pvt Ltd., India
MBA, EMBA (IIM)
Email: Sudhanshu.adaptive@gmail.com

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Declaration

This article is intended as an educational review that compiles currently available scientific knowledge, anatomical concepts, and diagnostic considerations related to Phakic (APL) sizing. The content is presented as a structured collection of guiding factors derived from published literature, clinical understanding, and commonly accepted ophthalmic principles.

The material provided in this document is not intended to serve as a prescriptive clinical recommendation, treatment protocol, or mandatory guideline for refractive surgery. The information is designed solely to support medical practitioners, ophthalmologists, and eye surgeons in understanding the anatomical parameters, measurement technologies, and evolving analytical approaches that may influence Phakic (APL) size selection.

Clinical decision-making in refractive surgery involves multiple patient-specific factors, including but not limited to individual anatomical variability, clinical findings, diagnostic interpretation, surgical experience, and professional judgment. Therefore, the final decision regarding suitability, lens sizing, surgical technique, and patient management remains exclusively the responsibility of the treating eye surgeon, who acts as the ultimate authority (apex decision-maker) in determining the most appropriate course of action for each individual patient.

This document should be interpreted as an academic and educational resource intended to facilitate scientific understanding and discussion among qualified healthcare professionals. It does not replace clinical judgment, institutional protocols, regulatory guidance, or individualised patient assessment.

The authors and publishers do not assume responsibility for clinical outcomes resulting from the application or interpretation of the information contained herein. All clinical decisions should be made by appropriately qualified ophthalmic surgeons based on comprehensive evaluation of each patient and in accordance with accepted standards of medical practice.

Image Source Acknowledgement (Short)

Images and diagrams used in this article are included only for educational and scientific illustration purposes. The figures are schematic or concept-based representations derived from commonly available scientific and anatomical references related to anterior segment imaging and Phakic (APL) sizing.

We acknowledge and thank the sources and the ophthalmic scientific community whose published concepts have supported the understanding of:

anterior segment anatomy
OCT and UBM imaging principles
vault concept
3D anterior segment modelling

All trademarks and imaging technologies mentioned belong to their respective owners. No proprietary clinical images or confidential patient data have been used.

These illustrations are intended solely to support learning and scientific communication.

Refractive Surgery : Phakic intraocular lens (APL) Sizing Role of Anatomical Parameters, Advanced Imaging Technologies, and Artificial Intelligence–Based Prediction

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