Week 3: Alessandra Coogan

    Alessandra Coogan | Clinical Mentor: Dr. Scott Rodeo

    On Friday (6/12) I had the opportunity to shadow Dr. Scott Rodeo in the OR. One of the most rewarding aspects was seeing the full continuum of care—from clinic to surgery. Earlier in the week, I had met a patient in clinic who later underwent a distal biceps tendon repair, allowing me to see how treatment decisions translate into surgical intervention.

    I observed two ACL reconstructions, one using a patellar tendon graft and the other using a hamstring tendon graft (Figure 1). I learned that patellar tendon grafts are often preferred for younger athletes because they generally have lower failure rates. In some younger, high-risk patients, surgeons may also perform an additional stabilization procedure called a lateral extra-articular tenodesis (LET) to improve rotational stability and reduce the risk of re-tearing the ACL. It was fascinating to watch the graft preparation process, where the harvested tendon is folded and sutured repeatedly to achieve the desired thickness and strength before implantation.


Figure 1. ACL Reconstruction with Dr. Rodeo at HSS. (Top) Dr. Rodeo instructing three fellows on where to insert the arthroscopic probe. (Bottom) Visualizing the knee via arthroscopy. The glowing tube is the saline line pumping fluid into the knee.

    I was also able to observe a meniscus repair involving a longitudinal tear. After trimming damaged tissue from the tear margins, sutures were placed to compress the tissue and restore stability (with the hopes of healing). I was surprised by how tightly the tear was closed! It was visibly (and alarmingly, to me) bowed. A medial student explained that this was intentional, as the repair will relax over time and helps prevent the meniscus from catching on the articular cartilage during the healing process. Though it made sense, it was still a bit disconcerting. 

    Another interesting procedure involved a proximal biceps repair combined with the removal of a loose body from the shoulder. The loose body proved difficult to locate arthroscopically due to its position within the joint, but once found it measured roughly 1–2 cm across. Ow.

    During our weekly Tuesday meetings, Dr. Matthew Greenblatt presented on bone developmental biology. It was a packed lecture with very cool and informative information that reiterated just how incredibly and near-perfectly complex (and also fragile) the body is.

    On the research side, I have begun processing control datasets in Dragonfly (Figure 2). My current goal is to establish a standardized workflow for quantifying the total volume of the tibial plateau as well as the trabecular and subchondral bone compartments within the posterior medial region of the joint. This area experiences the greatest degeneration in our murine ACL injury model. 

Figure 2. 3D reconstruction and orthogonal views of a control right mouse knee generated from μCT scans in Dragonfly. The knee is shown in 3D (top left) and in sagittal (top right), axial (bottom left), and coronal (bottom right) cross-sections.

        Identifying the best segmentation approach has proven to be a bit tricky. While Dragonfly includes a Bone Analysis plug-in, it is not particularly well-suited for segmenting specific subregions of bone, especially when multiple bones are present within the same μCT dataset. To make matters more complicated, many of the available tutorial videos are several years old, making them difficult to follow when the software interface and recommended workflows have changed significantly in newer versions.

    As a result, I have begun experimenting with Dragonfly's deep learning tools. By manually segmenting a subset of slices and using them as training data, I have been able to generate pretty accurate automated segmentations. The main limitation is the time required to train the model and refine its predictions. Once I obtain experimental μCT datasets, I anticipate needing to retrain or fine-tune the model to account for disease-related changes in bone morphology. It will be interesting to see how well the workflow translates from healthy controls to diseased samples.

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