Depth relationships and measures of tissue thickness in dorsal midbrain.

Dorsal human midbrain contains two nuclei with clear laminar organization, the superior and inferior colliculi. These nuclei extend in depth between the superficial dorsal surface of midbrain and a deep midbrain nucleus, the periaqueductal gray matter (PAG). The PAG, in turn, surrounds the cerebral aqueduct (CA). This study examined the use of two depth metrics to characterize depth and thickness relationships within dorsal midbrain using the superficial surface of midbrain and CA as references. The first utilized nearest-neighbor Euclidean distance from one reference surface, while the second used a level-set approach that combines signed distances from both reference surfaces. Both depth methods provided similar functional depth profiles generated by saccadic eye movements in a functional MRI task, confirming their efficacy for delineating depth for superficial functional activity. Next, the boundaries of the PAG were estimated using Euclidean distance together with elliptical fitting, indicating that the PAG can be readily characterized by a smooth surface surrounding PAG. Finally, we used the level-set approach to measure tissue depth between the superficial surface and the PAG, thus characterizing the variable thickness of the colliculi. Overall, this study demonstrates depth-mapping schemes for human midbrain that enables accurate segmentation of the PAG and consistent depth and thickness estimates of the superior and inferior colliculi.

Performance of IOL calculation formulas that use measured posterior corneal power in eyes following myopic laser vision correction.

PURPOSE: To compare the predictive accuracy of the biometer-embedded Barrett True-K TK and new total corneal power methods of intraocular lens (IOL) power calculation in eyes with prior laser vision correction (LVC) for myopia. SETTING: Academic clinical practice. DESIGN: Retrospective case series. METHODS: IOL power formulas were assessed using measurements from a swept-source optical coherence biometer. Refractive prediction errors were calculated for the Barrett True-K TK, EVO 2.0, Pearl-DGS, and HofferQST, which use both anterior and posterior corneal curvature measurements. These were compared with the Shammas, Haigis-L, Barrett True-K No History (NH), optical coherence tomography, and 4-formula average (AVG-4) on the ASCRS postrefractive calculator, and to the Holladay 1 and 2 with non linear axial length regressions (H1- and H2-NLR). RESULTS: The study comprised 85 eyes from 85 patients. Only the Barrett True-K TK and EVO 2.0 had mean numerical errors that were not significantly different from 0. The EVO 2.0, Barrett True-K TK, Pearl-DGS, AVG-4, H2-NLR, and Barrett True-K NH were selected for further pairwise analysis. The Barrett True-K TK and EVO 2.0 demonstrated smaller root-mean-square absolute error compared with the Pearl-DGS, and the Barrett True-K TK also had a smaller mean absolute error than the Pearl-DGS. CONCLUSIONS: The Barrett True-K TK and EVO 2.0 formulas had comparable performance to existing formulas in eyes with prior myopic LVC.

Efficacy of segmented axial length and artificial intelligence approaches to intraocular lens power calculation in short eyes.

PURPOSE: In short eyes, to compare the predictive accuracy of newer intraocular lens (IOL) power calculation formulas using traditional and segmented axial length (AL) measurements. SETTING: Cullen Eye Institute, Baylor College of Medicine, Houston, Texas and East Valley Ophthalmology, Mesa, Arizona. DESIGN: Multi-center retrospective case series. METHODS: Measurements from an optical biometer were collected in eyes with AL <22 mm. IOL power calculations were performed with 15 formulas using 2 AL values: (1) machine-reported traditional AL (Td-AL) and (2) segmented AL calculated with the Cooke-modified AL nomogram (CMAL). 1 AL method and 7 formulas were selected for pairwise analysis of mean absolute error (MAE) and root mean square absolute error (RMSAE). RESULTS: The study comprised 278 eyes. Compared with the Td-AL, the CMAL produced hyperopic shifts without differences in RMSAE. The ZEISS AI IOL Calculator (ZEISS AI), K6, Kane, Hill-RBF, Pearl-DGS, EVO, and Barrett Universal II (Barrett) formulas with Td-AL were compared pairwise. The ZEISS AI demonstrated smaller MAE and RMSAE than the Barrett, Pearl-DGS, and Kane. K6 had a smaller RMSAE than the Barrett formula. In 73 eyes with shallow anterior chamber depth, the ZEISS AI and Kane had a smaller RMSAE than the Barrett. CONCLUSIONS: ZEISS AI outperformed Barrett, Pearl-DGS, and Kane. The K6 formula outperformed some formulas in selected parameters. Across all formulas, use of a segmented AL did not improve refractive predictions.