Comparison of Lens Extraction Versus Laser Iridotomy on Anterior Segment, Choroid, and Intraocular Pressure in Primary Angle Closure Using Machine Learning

Abstract

Précis: Treatment strategy of primary angle closure (PAC) is not clear due to the large number of clinical and anatomic-topographic parameters in PAC, influencing the treatment algorithm. Using the machine learning method DD-SIMCA, we justify the expediency of early lens extraction (LE) in PAC. Purpose: To compare the anatomic and functional efficacy of LE and laser peripheral iridotomy (LPI) in patients with PAC using Machine Learning. Materials and Methods: This prospective study included 120 patients aged 41–80 years: 60 eyes with PAC, 30 with PAC suspects, and 30 with healthy eyes (control). Thirty PAC eyes with intraocular pressure (IOP) up to 30 mm Hg were treated using LE with intraocular lens implantation and 30 eyes with LPI. All subjects underwent Swept Source optical coherence tomography. We analyzed 35 parameters of each eye including the lens vault, the choroidal thickness, the anterior chamber angle, and iris specifications such as iris curvature. Considering the correlations between them, the machine learning method DD-SIMCA 1-class classification was applied: the proximity of each sample to the target class (control) was characterized by the total distance to it. Results: After LE, IOP was significantly lower than after LPI (P=0). Every third eye with PAC after LE reached the target class: specificity according to DD-SIMCA equals 0.67. This was not observed for the eyes after LPI: specificity equals 1.0. After LE, all parameters of the anterior chamber angle did not differ from the control (all P>0.05). After LPI, there was an increase in anterior chamber depth (P=0) and a decrease in lens vault (P=0), but results comparable to the control were achieved only for iris curvature (P=1.000). Conclusion: The efficacy of LE in PAC is higher than LPI due to the better postoperative anterior chamber topography and lower IOP. This study lends further clinical and anatomic support to the emerging notion of LE as an effective treatment for PAC.

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

“…For example, AI has been used to compare glaucoma surgery techniques for a given glaucoma diagnosis by evaluating a large number of clinical and anatomic parameters that were previously difficult to assess. In a study using the DD-SIMCA method and machine learning to assess various clinical parameters, lens extraction was found more effective than laser iridotomy in treating primary angle-closure glaucoma (PACG), leading to improved anterior chamber topography and reduced IOP [122]. In the future, AI could be used to help generate valuable comparisons between surgical techniques, affecting clinical treatment decisions.…”

Analysis on the clinical correlation of lens parameters and choroidal thickness with the severity of primary angle closure disease

“…Moreover, clear-lens extraction (CLE) has been proved effective to reduce the risk of acute angle closure. [9][10] However, CLE or early cataract extraction as a de nitive treatment for delaying the progression of PACD is still debatable in the developing countries, considering the risks involved with PACD was classi ed based on International Society Geographical & Epidemiological Ophthalmology (ISGEO) classi cation. [11,12] Patients in whom the angle of anterior chamber was apposed or closed in greater than 270° in all three quadrants were included in the study.…”

Machine learning–couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy

“…The relevance of the treatment of PACD in the early stages before the formation of GON is recognized by many researchers [ 16 – 20 ]. However, the question concerning the method of choice for PAC treatment (LPI or lens extraction, LE) remains still open [ 21 ].…”